Philosophy

Since at least James Suroweicki’s “Wisdom of the crowds”, a multitude of scholars involved in the field of epistemic democracy have taken to theorizing epistemic utility of tools like “Prediction Markets”, and even the “Wikipedia” model. Cass Sunstein, a Professor of Law at University of Chicago, has in particular been effective in advocating the idea through a stylized analysis that cherry picks successfully working corporate prediction markets and ignores problems like the current morass of InTrade. Here below I analyze the conditions under which predictions markets can deliver their theorized epistemic gains, and test their robustness to violation of optimal conditions. I however start with analyzing a comparison that political scientist Josiah Ober makes between Ostracism and Prediction Markets and in doing so lay out some of the essential features of markets.

Josiah Ober and Learning from Athens

Ober has been a keen exponent of the idea that ancient Athens had institutions that ably aggregated information from citizens, and fostered “considered” judgments. In his Boston Review article, he strangely argues that the decision to build hundreds of warships, prodded by Oracle (!) and now known deliberate misinformation by Themistocles, led to an ultimately ‘right’ decision by the assembly to build warships and not say distribute the windfall from the silver mines to the average citizen. There are two problems here – one is epistemological with its reliance on Oracles for signs, and the other is use of manipulative information ala Cheneyesque. It is impossible to answer whether Persia attacked because they felt inklings of a threat due to the huge armada of ships that Athens had built.

At another place, Ober has compared the first step of Ostracism proceedings - the Demos taking a vote to determine whether to hold ostracism or not - with Prediction Markets. He argues that the vote to hold Ostracism or not aggregated individual level information or predictions about whether there is “someone” whose presence is pernicious enough so as to merit Ostracism. There are three pitfalls to such comparisons and I will deal with them individually. Firstly, Ostracism didn’t provide people with direct private economic incentives to reveal private information or seek “correct” information, and something which economists believe is essential (it is also born out in experiments). To counteract this argument, Dr. Ober argues that the manifest threat of making a “wrong” decision was large enough to impel citizens to gather the best information. There are two problems with this argument – penalties for making wrong decision fall on a continuum and are rarely either prosperity or annihilation (certainly the case in Themistocles and Persia), and secondly even in presence of immanent threats (something not quiet true in this case as the threat is defined vaguely as “wrong decision”- which is a little different from the most informed decision) to groups “collective action problem” prevails - albeit in an extenuated form.

In ancient Athens, decision to hold Ostracism or not was made through a vote. Vote is a deeply impoverished aggregator of private information for with each dip you get only a yes or a no. This impoverished information sharing also exerts enormous pressure on distribution of “right” information among the population for a small minority of “right” voters can easily be silenced by a misinformed majority. The only way in fact a Voting system can reliably aggregate information (if choice is binary) is if each dip –on average -has more than 50% chance of being correct. (Condorcet’s insight) Markets on the other hand provide for information to be expressed much more precisely through price. (We will come to the violations of this tenet in markets later.)

Unlike in voting, markets deter information (and misinformation unless strategically) sharing although price does send cues (information) to the market. (Of course strategic players fudge investments so as monetize their investment maximally) Suffice it is to say however that voting systems are more prone to aggregating disinformation, than market systems where incentives for gaining “right information” increase in tandem with people investing with “wrong information”.

Markets, Betting Markets

I will deal with some other issues including assumptions about distribution of private information later in the article. Let me briefly stop here to provide an overview of markets and betting markets in particular.

Markets, when working optimally, are institutions that aggregate all hidden and manifest information and preferences and express it in a one-dimensional optimally defined parameter, price. Since all individual preferences are single-peaked with reference to price, markets are always single-peaked, avoiding aggregation issues and Condorcet’s paradox. Markets aggregate not only information about demand, and supply but also the utility afforded by the commodity to each individual consumer, and such aggregation optimizes the “allocative efficiency”. And apparently all this is done magically – and in Adam Smith’s coinage at the beckoning of the famous “invisible hand”.

Prediction markets – also known under the guises of “information markets” or “idea futures” among others – tie economic gains to fulfillment of some prediction. The premise is that possibility of economic gain will provide people to reveal hidden information – or more precisely bet optimally without revealing information. Prediction markets are quiet different from regular markets for trading is centralized against a bookmaker that decides the odds after aggregating bets. This type of architecture puts significant constraints on the market than say the architecture of a share market, which is essentially decentralized. I will come to the nature of the constraints later but suffice it is to say that it avoids some of the “variances” and “excesses” and “excess variances” of the decentralized system – the kinds which made Robert J. Shiller turn to behavioral economics from playing with math and monkey wrench models.

Expanding on the nature of prediction markets – “A prediction market is a market for a contract that yields payments based on the outcome of a partially uncertain future event, such as an election. A contract pays $100 only if candidate X wins the election, and $0 otherwise. When the market price of an X contract is $60, the prediction market believes that candidate X has a 60% chance of winning the election.” (Prediction Markets in Theory and Practice -2005 Draft, Justine Wolfers and Eric Zitzewitz) A more robust description is perhaps necessary to explain how bookies come to know about these odds. In much of sports betting, bookies commence betting by arriving at consensus that reflects expert opinions of a small group of professional forecasters. “If new information on the relative strengths of opposing teams (e.g., a player injury) is announced during that week, the bookie may adjust the spread, particularly if the volume of behavior favors one of the teams. In addition, since the identity of the bettors is known, bookies may also change the spread if professional gamblers place bets disproportionately on one team. To make these adjustments, the bookie moves the spread against the team attracting most of the bets to shift the flow of bets toward its opponent. Shortly before game time, the bookie stops taking bets at the ‘closing’ point spread. Like securities prices at the end of trading, closing spreads are assumed to reflect an up-to-date aggregation the information and, perhaps, biases of the market participants.” (Golec and Tamarkin, Degree of inefficiency in the football betting market, 1991, Journal of Financial Economics: 30). There are other ways through which a similar arrangement can be executed. For example, computerized software now continually adjust the odds depending on bets. The danger is that you can quickly short the system if you solely rely on anonymous betting data. I will come back to his later. One additional point to finish the description - Given the nature of the commodities or assets traded, we can only get results on questions that have binary answers, and not say discovery questions unless discovery questions can be split into innumerable binary questions.

Before we analyze the betting market efficiency, I would like to present a short list of the previously theorized (and proven) betting market failures or “instances where the operation of the market delivers outcomes that do not maximize collective welfare.” There are several forms of market failure:

• Imperfect competition – where there is unequal bargaining power between market participants;
• Externalities – where the costs of a particular activity are external to the individual or business and imposed on others (e.g. Assassination Markets);
• Public goods – where there are goods for which property rights cannot be applied; and
• Imperfect information – where market participants are not equally informed.

* Taken from Objectives of Betting and Racing Legislation (doc)

*As always, penalties follow some function of the extent of violation. Most effects are non-linear.

Let’s analyze the epistemic dimension of the market as in its capability to deliver information that is somehow better. The supposition in a prediction market is that people aren’t revealing (or finding information) for they have inadequate incentives to do so. So betting is merely a way to incentivize the discovery process. It is important to note that merely the fact it assumes that people have private reserves of information (generally amounting to knowledge that other people aren’t smart) severely limits the role of the prediction markets in areas where there isn’t such knowledge. Certainly I can’t think of a lot of public policy arena where it is the case. (It is also important to keep in mind that most policy decisions have a normative dimension aside from some fully informed preference dimension.) Otherwise betting markets merely try to aggregate – and don’t do so well – public information cues. Simon Jackman in his forthcoming paper that analyzes betting behavior in political markets in Australia has found that betting markets essentially move following the cues of opinion polling results. There is no information source outside of what is already publicly accessible that people rely on to make their bets. So the idea that somehow prediction markets will deliver better results even where privately held reserves of information are low or zero is ludicrous and easily empirically disproved.

More importantly, betting markets – even sophisticated ones like the sports betting markets – are incurably biased - proven statistically multiple times over – they underestimate home field advantage, and all too often “go with the winners”. The bias is supported by two intertwining psychological biases - “safe betting” and “betting on favorites” – and it is a bias found in nearly all betting markets.

Betting markets behave best if there is complete adversarial betting – which is never the case for most of the price is set by investment by small players following the elite herd. This has defined by Sushil Bikhchandani, David Hirshleifer and Ivo Welch, in a classic 1992 article, as “information cascades” that can lead people into serious error. Shiller recently wrote about this while explaining how the housing bubble (essentially banks betting on loans) stayed under the radar so long. He quotes the paper at length –

“Mr. Bikhchandani and his co-authors present this example: Suppose that a group of individuals must make an important decision, based on useful but incomplete information. Each one of them has … information…, but the information is incomplete and “noisy” and does not always point to the right conclusion.

Let’s update the example…: The individuals in the group must each decide whether real estate is a terrific investment… Suppose that there is a 60 percent probability that any one person’s information will lead to the right decision. …
Each person makes decisions individually, sequentially, and reveals … decisions through actions — in this case, by entering the housing market and bidding up home prices.

Suppose houses are really of low investment value, but the first person to make a decision reaches the wrong conclusion (which happens, as we have assumed, 40 percent of the time). The first person, A, pays a high price for a home, thus signaling to others that houses are a good investment.

The second person, B, has no problem if his own data seem to confirm the information provided by A’s willingness to pay a high price. But B faces a quandary if his own information seems to contradict A’s judgment. In that case, B would conclude that he has no worthwhile information, and so he must make an arbitrary decision — say, by flipping a coin to decide whether to buy a house.
The result is that even if houses are of low investment value, we may now have two people who make purchasing decisions that reveal their conclusion that houses are a good investment.

As others make purchases at rising prices, more and more people will conclude that these buyers’ information about the market outweighs their own.

Mr. Bikhchandani and his co-authors worked out this rational herding story carefully, and their results show that the probability of the cascade leading to an incorrect assumption is 37 percent. … Thus, we should expect to see cascades driving our thinking from time to time, even when everyone is absolutely rational and calculating.

This theory poses a major challenge to the “efficient markets” view of the world… The efficient-markets view holds that the market is wiser than any individual: in aggregate, the market will come to the correct decision. But the theory is flawed because it does not recognize that people must rely on the judgments of others. …

It is clear that just such an information cascade helped to create the housing bubble. And it is now possible that a downward cascade will develop — in which rational individuals become excessively pessimistic as they see others bidding down home prices to abnormally low levels. “

Betting markets like all other markets are “sequential” with each investor trying to parse tea leaves and motives of prior investors. The impulse to do original research is counterveiled by the costs, and by the fear that others know something that they don’t.

The other intersecting psychological factor that complicates markets is complete blind betting. Time and again even as information and probabilities converge, some bettors hold out for a miracle.

It is also important to keep in mind the following tenet that governs prediction market behavior– “Garbage in, garbage out… Intelligence in, intelligence out…” So prediction markets – to the extent that they rely on speculation are remarkably likely to follow any information that is likely to give them a leg up. While misinformation theoretically incentivizes procurement of good information, it never pans out empirically for major investment by another is seen as an informational cue, more powerful than whatever access you may have. This is an important point – for the competitor has no way of knowing your information for all s/he has access to is the investment that you make on it, and the space for conjecture about the veracity of the competitor’s information is immense. This is a market based on never revealing information, and that diminishes the efficiency considerably.

Betting markets merely rely on the fact that you are less misinformed than others, and that gradient can be built through strategically spreading misinformation (quiet common in betting circles) or through some theorized virtuous cycle of increasingly good information.

Betting markets can be easily shot by someone willing to lose some money. Asymmetry in finances can hobble the incentives for betting market and information discovery process.

Lastly, laws against insider trading limit the kind of information bettors have access to. They limit information discovery process severely. Relatedly, information – for it to be monetizable – has to be brought into the system privately so bettors may try to sabotage release of public information. Not only that, they have to be strategic in how they send cues to the market so that they earn the most money from their bets. If done en masse or rashly, it will almost certainly short their bets. So not only do betting markets have only one way of expressing information – price/investment- bettors go to great lengths to hide that cue especially if they know how the cues are being aggregated.

In summary, the above list of problems with betting markets underscores the analytical and empirical evidence against the naïve ill-substantiated unbridled faith in the epistemic prowess of the betting markets.

First Amendment mandates that “Congress shall make no law … abridging the freedom of speech …” Courts have over the years construed the clause in a way that privileges political speech in its various manifestations while ruling to limit the protections afforded to commercial speech. Hence - however limitedly – different regulatory frameworks have emerged to control commercial speech in a variety of arenas. So for example, FDA through its Division of Drug Marketing, Advertising, and Communications sets standards for all drug advertisements. For example, it mandates that “all drug advertisements contain (among other things) information in brief summary relating to side effects, contraindications, and effectiveness.” Similarly any company listed publicly has to comply with a host of disclosure laws about its finances and business practices that dramatically restrict the kind of claims companies can make – at least about their accounts. The principle behind these mandates is the understanding that public good (in case of stock market – investor good) is served when we limit or penalize disinformation. The further pretext for such laws is that such laws are necessary where supply of information is essentially a monopoly of organizations or people with explicit incentives to lie or strategically misrepresent information.

Arguments for setting epistemic standards for speech in the political arena have been criticized in America on a variety of grounds including that such a law will be unduly restrictive and hard to implement, that free speech is necessary for democracy, free speech is a “fundamental” “human right” (absent of its necessity or utility) – it is part of Article 19 of UN Declaration of Human Rights, and free speech promotes search for truth, etc. More broadly, there are two major defenses for freedom of speech - a deontological conception of the sanctity of freedom of speech (expression – more broadly), and an instrumental defense of its merits – its necessity for preserving democratic values and ideals etc. It is hard to contest either of the claims: the claim for normative supremacy is hard to make in face of axiomatic judgments about the “fundamental” nature of these “rights”, and instrumental supremacy is hard to argue given “democratic values and ideals” are so loosely defined that they can be spun every which way, and an incommensurable value attached to any of those parts. But let’s hold this chain of thought for now.
Black’s Law Dictionary, 5th ed., by Henry Campbell Black, West Publishing Co., St. Paul, Minnesota, 1979, defines fraud as, “All multifarious means which human ingenuity can devise, and which are resorted to by one individual to get an advantage over another by false suggestions or suppression of the truth. It includes all surprises, tricks, cunning or dissembling, and any unfair way which another is cheated.”

It is an unsurprisingly vague definition cognizant of the artfulness of the subtlety with which fraud is perpetrated. The law relies on the skill of the prosecutor, and in company’s case – the defense attorney, and the perspicacity of the judge (or) jury to come up with a judgment as to whether fraud was perpetrated. One can come up with a more restrictive definition of the “law” but it would most likely be counterproductive for it would channel effort in perpetrating “fraud” that is still technically legal – much like compliance with tax law through using tax havens – and by taking away discretion from the judges, make it impossible to award judgments against patent cases of fraud. In the political arena, people are justifiably skeptical of arriving at limiting definitions, and at submitting speech to be analyzed by a body with fairly large discretionary limits on interpretation of the “letter of the law”. More nefarious motives undoubtedly exist for such protestations – least of them perhaps include worries that such a law would jeopardize their chances at electoral success. Then there is empirical evidence to suggest that politicians are very skillful at being honest without ever telling the truth. A stylistic narrow minded honesty that includes choice picking of their own life and opposition’s words is currently in vogue, and very hard to guard against. Not to mention the perverse ability to bludgeon the voter with the inconsequential, or ability to strategically shift focus on issues. For example – the 2000 campaign was essentially fought on the grounds of “whom would one like to have beer with?”, or the “Willie Horton” ad used in 1988 election featuring real life person and story – though of limited evidential value - to skillfully convey race and crime cues to the disadvantage of the Democratic challenger, Michael Dukakis.

Quiet apart from the justifications for “freedom of speech” is the claim that we should let the market dictate roughly the proportion of what is heard at what volume, and what isn’t. The supposition is that market will somehow tune up the volume of the speech in proportion to its appeal to people – with no claims made about its epistemic worth. The ancillary argument is that “marketplace of ideas” will sift through ideas in a way that the “best” ideas and opinions rise to the top, and additionally even if someone buys more airtime it doesn’t quiet matter for the public decides whether the idea is in its interest and hence dictates its adoption (popularity). Understandably it is a hopelessly unsupported proposition lacking any merit whatsoever.

The argument that competition alone would be enough to bring the “product” with the “best utility” (economic utility) to come to the top is doubtful at best, and predicated on a host of bizarre wholly empirically unsupported assumptions. The argument is particularly inapplicable to the domain of “public goods” where people have limited incentives to gather enough information, and in systems where information distribution is asymmetric. Anthony Downs expressed worries about “asymmetric information” in his 1957 opus, An Economic Theory of Democracy. There is indeed a steadily increasing penalty for disinformation and the resulting sub-optimal decisions, but given the low efficacy that people feel (among other things), their interest and motivation remains in making themselves more informed low. Given such conditions, we need – more strongly than a regulatory framework governing information dispersal in private economic choices – a similar mechanism so as to mitigate some of the most severe problems.

One of the ways - and one that would be appealing to all political parties and free speech advocates - through which we can mitigate some of the problems in the current information regime would be to mandate release of government information. Transparency has been found to increase attendance of teachers in rural schools, and distribution of funds to people in rural employment scheme in India, attendance of legislators in Uganda etc. FOIA works to a great degree in US and it is arguable that the gravest trespasses occur where the reach of the statute is the most limited – “national defense”. Transparency is effective because it creates opportunities for accountability. It however also foments strategic compliance. For example, as I mentioned earlier – we now have fairly truthful ads that systematically misrepresent issues and positions of opposition. Graver still is the issue that transparency only works to a limited degree in a country where people are apathetic, and media absconding. For example, while America has inarguably the largest trove of publicly available data including statistics on economics, labor etc. – they are hardly ever part of the public discourse. The corrective solution flows directly from the way I describe the problem –creating an aware media that works to highlight these facts and put issues and positions in context.

Let me take a moment to argue that given mass media is currently the dominant way through which people get information, any proposal for instituting epistemic controls has to cover media. Similarly, any proposal for epistemic controls has to not only ensure epistemic superiority of the resulting commentary, but also regulate how it is presented so as to be useful to the masses. In other words, the framework has to take into account the state of the masses, and how they process information. Given the psycho-cognitive research by Kahneman and others, we know that people regularly ignore base rate information in favor of illustrative anecdotes - the way news is traditionally offered. We also know enough through the priming literature that repeatedly bringing up an issue –regardless of context – increases its salience in decision making.

Given the difficulty in mounting such controls, perhaps the best epistemic intervention that we can provide is an educative one. There is ample evidence that if we improve people’s understanding of what constitutes valid and pertinent evidence, and even help inculcate simple skills like numeric literary - we can have a tangible impact on the way people make decisions and how they respond to appeals.

King et al. (1994: 75, note 1): ‘[a]t its core, real explanation is always based on causal inferences’.

Limiting Discussion to positivist Qualitative Methods

Qualitative Methods can be roughly divided into positivist (case-studies etc.) and interpretive. I will limit my comments to positivist Qualitative Methods. The differences between positivist qualitative and quantitative methods ‘are only stylistic and are methodologically and substantively unimportant’ (King et al., 1994:4). Both methods share ‘an epistemological logic of inference: they all agree on the importance of testing theories empirically, generating an inclusive list of alternative explanations and their observable implications, and specifying what evidence might infirm or affirm a theory’ (King et al. 1994: 3).

Causal Inference in Empirical Data

To impute causality, science relies either on evidence based process knowledge, or clever experiment design that obviates (perhaps more correctly, mitigates) the need to know the process – though researchers often are encouraged to have a story to explain the process, and test variables implicated in the ’story’.

Experimentation provides one of the best ways to reliably impute causality. However for experiments to have value outside (the labs), the ‘treatment’ must be ‘ecological’ as in reflect the typical values that the variables takes in the world –for example, effect of news is best measured with either real life news clips or something similar, and the findings must hold up in the field (through surveys/field experiments). The problem is that most problems in Social Science cannot be studied experimentally. Brady et al. (2001:8) write: ‘A central reason why both qualitative and quantitative research are hard to do well is that any study based on observational (i.e., non-experimental) data faces the fundamental inferential challenge of eliminating rival explanations’. While most Social Science papers (and certainly practitioners) talk about ‘eliminating’ rival explanations, one doesn’t quiet have to do that. Social Scientists often times include variables reflecting ‘rival explanations’ as ’straw variables’ (to refer to the straw man they will blow at the end) in a regression equation, to show how much variance is ‘explained’ by their variable of choice as compared to the ’straw variables’.

In Quantitative Methods, to impute causality, one makes a variety of assumptions including: a ‘ceteris paribus’ (all other things being equal – which may mean assigning away everything ‘else’ to randomization) clause, error term (non-systematic) part is not correlated with other independent variables, to infer the correlation between an explanatory variable x and dependent variable y can only be explained as x’s effect on y. In analyzing survey data, one ‘controls’ for variables using regression, or some other similar way. There are a variety of assumptions in regression models and penalty for violation of each of these assumptions. In Qualitative Methods, one can either analytically (or where possible empirically) control for variables, or trace the process.

Qualitative Methods: some handles on generalizability

Traditional probability sampling theories are built on the highly conservative assumption that we that we know nothing about the world. And the only systematic way to go about knowing it is through ‘random sampling’, a process that delivers ‘representative data’ on average. Newer sampling theories, however, acknowledge what we know about the world by selectively over-sampling things (or people) we are truly clueless about, and under-sampling where we have a good idea. For example, polling organizations under-sample self described partisans, and over-sample non-partisans. This provides a window for positivist qualitative methods to make generalizable claims. Qualitative methods can overcome their limitations and make legitimate generalizable claims if their sampling reflects the extent of prior knowledge about the world.

Other than sampling, there are other analytical ways of getting a handle on the variables that ‘moderate’ the effect of a particular variable that we may be interested in studying. For example, we can analytically think through how education will affect (or not affect) racist attitudes. Analytical claims are based on deductive logic and a priori assumptions or knowledge. Hence the success of analytical claims is contingent upon the accuracy of the knowledge, and the correctness of the logic.

One of the problems that have been repeatedly pointed out about Qualitative research is its propensity to select on the dependent variable. Selection on dependent variable deviously leaves out cases where for example the dependent variable doesn’t take extreme values. Selection bias can not only lead to misleading conclusions about causal effects but also about causal processes. It is important hence not to use a truncated dependent variable to do one’s analysis. One of the ways one can systematically drill down to causal processes in qualitative research is by starting off with the broadest palette, either in prior research or elsewhere, to grasp the macro-processes, and other variables that may affect the case. Then cognizant of the particularistic aspects of a particular case, analyze the microfoundations or microprocesses present in the system.

Positivism and Empiricism

Scientific method roughly refers to systematic analysis of empirical data. The quality and the strength of the ’system’ - forever open to challenge - determine any claims of epistemic superiority that the ’scientific method’ may make over other competing claims of gleaning ‘knowledge’ from data. The extent to which claims are solely arbitrated on scientific merit is limited by a variety of factors, as outlined by Lakatos, Kuhn, and Feyerabend, resulting in at best an inefficient process and at worst something far more pernicious. I however ignore such issues and focus narrowly on methodological questions around causality and generalizability in qualitative methods.

In science, inquiry into generalizable causal processes is greatly privileged, and for good reason - causality and generalizability taken together can provide basis for policy action, for mounting intervention, among other things. One can also think of knowledge of causal processes as providing predictive power. However, not all kinds of data make themselves readily accessible to imputing causality, or even to making generalizable descriptive statements. For example, causal inference in most historical research remains out of bounds. AddKeeping this in mind, I analyze how qualitative methods within Social Sciences (can) interrogate causality and generalizability.

Causality:

Hume felt that there was no place for causality within empiricism. He argued that the most we can find is that “the one [event] does actually, in fact, follow the other”. More broadly, causality is nothing but an illusion occasioned when events follow each other with regularity. That formulation however didn’t prevent Hume from believing in scientific theories for he felt that regularly occurring constant conjunctions were sufficient basis for scientific laws. Theoretical advances in the 200 or so years since Hume have been able to provide a deeper understanding of causality, including a process based understanding and an experimental understanding.

Donald Rubin, a Professor of Statistics at Harvard, defines causal effect as, “Intuitively, the causal effect of one treatment, E, over another, C, for a particular unit and an interval of time from t1 to t2 is the difference between what would have happened at time t2 if the unit had been exposed to E initiated at t1 and what would have happened at t2 if the unit had been exposed to C initiated at t1: ‘If an hour ago I had taken two aspirins instead of just a glass of water, my headache would now be gone,’ or because an hour ago I took two aspirins instead of just a glass of water, my headache is now gone.’ Our definition of the causal effect of the E versus C treatment will reflect this intuitive meaning.”

It is important to note that RCM as presented above depicts an elementary causal connection between two Boolean variables: one level of explanatory variable (two aspirins) with single effect (headache is gone). Often times, the variables take multiple values and to study that, we need to mount a series of experiments. Similarly one may want to analyze the effect of a variable in different subgroups of populations, for example, effect of aspirin on women as compared to men. All the above two scenarios do is highlight the problems in coming up with a robust understanding of causal processes between (here) two variables. Fundamentally though, our RCM understanding of causal effect remains unchanged.

Rubin Causal Model provides a counterfactual deterministic understanding of causality that is firmly based in the logic of experiment design. RCM formulation can be expanded to include a probabilistic understanding of causal effect. Just as a note: A probabilistic understanding of causality implicitly accepts that certain parts of the explanation are still missing, and hence is absent of a necessary and sufficient condition though attempts have been made to include necessary and sufficient clauses in probabilistic statements. David Papineau (Probabilities and Causes, 1985, Journal of Philosophy) writes, “Factor A is a cause of some B just in case it is one of a set of conditions that are jointly and minimally sufficient for B. In such a case we can write A&X ->B. In general there will also be other sets of conditions minimally sufficient for B. Suppose we write their disjunction as Y. If now we suppose further that B is always determined when it occurs, that it never occurs unless one of these sufficients sets (let’s call them B’s full causes) occurs first, then we have, A and X condition conjugated with Y is equivalent with B. Given this equivalence, it is not difficult to see why A’s causing B should be related to A’s being correlated with B. If A is indeed a cause of B, then there is a natural inference to Prob(B/A) > Prob(B/-A): for, given A, one will have B if either X or Y occurs, whereas without A one will get B only with Y. And conversely it seems that if we do find that Prob(B/A) > Prob(B/-A), then we can conclude that A is a cause of B: for if A didn’t appear in the disjunction of full causes which are necessary and sufficient for B, then it wouldn’t affect the chance of B occurring.”

Papineau’s definition is a bit archaic, and doesn’t quite cover the set of cases we define as probabilistically causal. John Gerring (Social Science Methodology: A Criterial Framework, 2001: 127,138; emphasis in original), provides a definition of probabilistic causality: ‘[c]auses are factors that raise the (prior) probabilities of an event occurring. (…) [Hence] a sensible and minimal definition: X may be considered a cause of Y if (and only if) it raises the probability of Y occurring.’

A still more ’sensible’ and still ‘minimal’ definition of causality, can be found in Gary King et al. (Designing Social Inquiry: Scientific Inference in Qualitative Research, 1994: 81-82), ‘the causal effect is the difference between the systematic component of observations made when the explanatory variable takes one value and the systematic component of comparable observations when the explanatory variable takes on another value.’

Brief discussion on Causal Inference in Qualitative and Quantitative Methods

While the above formulations of causality – Rubin Causal Model, Gerring, and King – seem more quantitative, they can be applied equally to qualitative methods. A parallel understanding of causality, used much more often in qualitative social science, is a process based understanding of causality wherein you trace the causal process to construct a theory. Simplistically speaking, in Quantitative methods in Social Sciences, one often times deduces the causal process, while in Qualitative methods the understanding of the causal process is induced from deep and close interaction with ‘data’.

Both deduction and induction processes, however, are rife with problems. Deduction privileges formal rules – statistics – that straightjacket the systematic deductive process so that the deductions are systematic and cognizant of explicit assumptions (like normal distribution of data, linearity of the effect, lack of measurement error, etc.). The formal deductive process bestows a host of appealing qualities like generalizability (when an adequate random sample of population is taken) or even systematic handle on causal inference. In quantitative methods, the methodological assumptions for deduction are cleanly separated from data. The same separation - between the formal deductive process with a rather arbitrarily chosen statistical model and data – however makes the discovery process less than optimal, and sometimes deeply problematic. Recent research by Ho and King (Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference, Political Analysis, 2007), and methods like Bayesian Model Averaging by Volinsky, have gone some ways in providing ways to mitigate problems with model selection.

No such sharp delineation between method and data exists in qualitative research, where data is collected iteratively – [in studies using iterative abstraction (Sayer 1981, 1992; Lawson 1989, 1995) or grounded theory (Glaser 1978; Strauss 1987; Strauss and Corbin 1990)] - till it explains the phenomenon singled out for explanation. Grounded data driven Qualitative methods often run the risk of modeling in particularistic aspects of data, which impedes the reliability with which they can come up with a generalizable causal model. This is indeed only one kind of qualitative research for there are others who do qualitative analysis in the vein of experiments – for example with a 2×2 model, and yet others who will test apriori assumptions by analytically controlling for variables in a verbal regression equation to get at the systematic effect of explanatory variable on the explanandum. Perhaps more than grounded theory method, the pseudo-quantitative style qualitative analysis runs the risk of coming to deeply problematic conclusions based on the cases used.

The following article has been written by Chaste, who has contributed to this blog before.

I will assume for the purposes of this piece that argument is the dominant form of writing in academia. This is also true of public discourse, but this piece will limit itself to academic writing, and more specifically to writing in the social sciences. If academia aims to produce worthwhile knowledge then the argument is the form that mediates our view of the object studied, and academia is the structure that mediates the production of the specific types of knowledge.

Mediating forms are layers of abstraction that can help understanding. They define a process for understanding that prevents distortion due to visceral or other perceptions. Yet as Adorno warns us, forms of viewing and the structures that regulate those forms can limit our understanding of the object studied. To take a simple instance, researcher-teachers argue a position to get published, and the success of such arguments in staking out and establishing a specific position within the field gets them promoted. Yet we expect researcher-teachers to conduct independent research, and train students to think with an open mind and to acquire comprehensive knowledge in a field. We must be wary lest the form of the argument and the structure of academia advance original/individualistic positions at the expense of responsible scholarship. We must also worry about the lure of political power for academics. Politics desires simple, narrow and sharply defined positions, whether for political posturing or for public programs. Succumbing to this lure can only exacerbate the effects of argument on knowledge.

Nature of Argument and its Role in Academia

I will quickly lay out the nature of the argument as used in academia. The argument typically takes the form of a declared purpose, followed by a description of the theoretical method, which the researcher will use for his analysis. It then applies this method to the selected data to come up with a conclusion that mirrors the declared aim. The primary critique of this form of argument uses a simple piece of reasoning. Applying a theoretical method to a selection of data should always produce the same result and conclusion. Therefore if “reasonable” (sic) minds disagree, this can only be because their aim / conclusion has pre-determined their choice of theoretical method and / or their selection of data.

The primary virtue of argument is its clarity. This is due to two qualities: consistency and discreteness. Yet consistency provides much greater explanatory power when we combine it with complexity than when we use it to support a single point. Argument often achieves discreteness by excluding other perspectives. We can gain greater explanatory power by defining relations between disparate issues perspectives, rather than by limiting them. “Cohere” is useful as it suggests both cohesion and coherence.

The popularity of the argument is doubtless because it mimics similar forms in science and law. Despite its recent success, scientific research is hardly a model of philosophical rigor. Recall the search for the “gay gene,” where scientists confused the biological phenomenon of sexual urge with the likely social phenomenon of sexual attractiveness. Or witness the ongoing fiasco about the continuous upward revision of global warming estimates because scientists had missed such obvious factors as the methane release from a permafrost melt, or missed the systematic differences caused by changes in methods for measuring temperature over the past century. Most scientific research is simple because it deals in the existence of facts. Reporting the properties of an element at 1000c has much scientific value. On the other hand, reporting my thoughts at any given moment has little value even though it may be an appropriate object of study for psychology or political science. Most scientific research does not involve speculative selection and aggregation of data. When it does, as in astronomy, public health, and climatology, its conclusions are no more reliable than those in the social sciences. “Speculative” is crucial when dealing with a seemingly infinite number of variables and infinite data. It underlines the necessity of an a priori judgment when faced with infinite variables and data. The analysis then turns into a test of the speculative judgment. It is at this point that ethics become crucial in such research. The researcher can choose his theoretical methods and select his data to prejudge the outcome in favor of the a priori judgment. Alternatively, the researcher can choose to be ethical. He could either come up with a new hypothesis and test it, or he could simply state the conclusion supported by the most appropriate selection of data and choice of methods.

The argument also mimics the dominant form used by law. However, the legal context is also quite different from that in social science research. It is true that lawyers select and even slant the data. However, the data is very limited. Again, the lawyer has no choice in the theoretical methods. Every legal point in dispute has a clearly established set of legal elements, which the lawyer must prove. It is this systematic exhaustion of every relevant legal element that makes legal briefs both thorough and thoroughly tedious. In academic arguments, exhaustive lists of theoretical methods are not possible. The infinite data and variables provide infinite scope for cherry picking: the only criterion is that the method or data support the argument. Cross-disciplinary studies exacerbate these dangers. They make it easier to ignore any agreement in a discipline about the criteria for selecting relevant data, or about the best methods to analyze a particular type o data. In this instance, the proliferation of theoretical methods simply provides even more tools to derive a conclusion of one’s choosing. An extreme case of this phenomenon is that of the cross disciplinary case study, which narrows the selection of data to a single instance, providing fertile ground for any conclusion whatever.

I do not suggest that such lax cross-disciplinary studies or case studies do not have any value. Their value is that of interpretive works, and is similar to the value of literary works. Literary works sometimes reveal networks of meaning that cannot b openly discussed in their time. Cross-disciplinary case studies can straddle the boundary between what can and cannot be viably discussed in academia. As such, they can provoke thought and provide insights and skeletal structure for further analysis. An excellent work of this kind is Patricia Williams’ “The Alchemy of Race and Rights,” which weaves together insightful arguments and moving, thought-provoking life experiences on various issues.

Another critical difference is that the law uses argument within an adversarial context. There is no counterpart in academic writing wherein a counter-argument immediately follows the argument. In this sense, an academic piece resembles an ex parte hearing in which the lawyer is required to disclose all facts favorable to the absent opponent. No such requirement exists in academic writing.

When used in the unsuitable academic context, the primary disadvantage of an argument is that it induces a loss of perspective by giving disproportionate emphasis to the position argued for. To a reader without extensive knowledge of the field, it is not possible to infer anything about the validity or worth of any claims. Unless the argument is part of a conversation, the only appropriate response is skepticism and a reserving of judgment. Judged by the credibility of its claims, the argument becomes worthless except as a data point in a survey or as part of a meta-reading.

Reforming the Argument

I will briefly examine a few alternatives to the argument as typically practiced. The first option is to abandon the argument in favor of a meditation that densely weaves together patterns of related insights. Meditation need not imply any loss of evidentiary or logical rigor, merely a flexible structure. The form of the meditation has several advantages. It dramatically reduces the problem of disproportionate emphasis on the one position. The substantive part of most arguments is less than 20% of their length. The rest is low value elaboration masquerading as thoroughness. The more flexible structure of the meditation will encourage authors to replace the low value elaboration with related information / insights. The weaving of patterns will become a pedagogical exercise, which will train the reader to map and relate the data in the field.

Meditation may be the ideal form; its practice is very likely to be something else. Authors will be tempted to spawn patchworks of recycled insights. This will make the editor’s job both time consuming and difficult.

Another solution involves a minor modification of the form of the argument. Editors can insist that the articles be self-aware: that they demonstrate how different conclusions can be drawn with different selections of data or variables, and different choices of theoretical methods. This is the least resource-intensive of my three solutions. However, it runs the risk that the author will demonstrate only those alternatives that support rather than undermine his conclusion.

The most pragmatic solution is to have peer-reviewed publications. Unfortunately, most “peer reviewed” journals are a misnomer since they are only peer approved. The journals should publish the peer reviews, and allow the author to respond to the reviews. The reviews themselves should be an engagement with the material, and not merely indicate the quality of the piece. The editors must choose reviewers from different methodological expertise and disciplinary backgrounds (where that is appropriate). This will deter the author from making expedient selections of data and of theoretical methods and traditions. It will also give the reader an adequate perspective on the author’s argument. The interactive nature of the reviews and response will have pedagogical value not only for the reader but also for the author and the reviewers. It will enable the author and reviewers to possibly expand on the conversation outside the journal, and provide networking-related scholarly and career benefits. The article will come with a relatively objective assessment of its worth, which will help both readers and anyone else who may be interested in evaluating the work of the author. The editors should keep the approval process separate from the review process, and keep the submissions to reviewers anonymous. The editors should take care that the reviews not mimic the journalistic practice of seeking input from hacks representing stereotypes of established positions. However, this is achieved relatively easily in academia.

Peer review is reduced to peer approval in the sciences because unlike the social sciences, scientific research often deals in results rather than conclusions, and because the researcher is the only person with direct access to the results. Therefore, peers in sciences are largely concerned with fraudulent claims of results rather than with the validity of conclusions. In social sciences, the data is often public, and the value of the article lies primarily in the validity of the conclusions. Peer review will help determine this validity. It will also encourage responsible scholarship from authors. Not only will every article have to survive a more rigorous engagement; this engagement will be invigorating for all. Above all, it is achievable in practice because it does not impose unacceptable burdens on editors.

Social scientists are technology’s historians, anthropologists, sociologists, and scientists – measuring the social impact of technology. It is important to note that all this activity is forever doomed to survive in the echo chambers of the forgotten consciousness of society and consigned to only enter in casual desultory (or heated but always ineffectual) discussions. Social scientists also produce knowledge that is directly useful to Capitalism and there of course it plays a more important role.

Society is led by the 800 pound gorilla of Capitalism and the ‘logic’ of market, which is quite separate from the ‘logic’ of social good, determines what is sold, how it is sold, and when. Social scientists merely study effects of new technologies as they are unleashed on the world. Universities open up new schools, departments, disciplines, and certainly new topics within disciplines as technology and reality march on. Take for example the following - two decades after television became a common amongst US households, David Phillips found evidence for ‘Werther effect’ like phenomena that linked suicides in real life to television suicides, and later still Robert Putnam linked heightened social alienation to television, and now there is a slew of literature detailing negative impact of violence on television. Of course nobody ever thought that they might want to research the impact of something before it is released.

Social scientists are consigned to doing research that will only rarely wend its way to policy making. And of course they will never get a chance to determine the course of technological growth, or other policies for those are tethered to Capitalism.

So what is the role of social science or for that matter science aside from helping create wealth, and helping society in a small number of cases where money making and social good coincide? There is really none in a world where increasingly the word regulation is seen as plague.

Perhaps we can write our own epitaph – we wagged our fingers and tongues, and scribbled furiously, as the chasm between the economic engine and the social good widened and Capitalism swallowed us whole. We also made some money doing that.

Preface

Epistemic standards for evidence delineate the kind of decisions reached in a decision making system though different systems need different kinds of explicit statutes for evidence to reach decisions of same ‘quality’ on average. Explicit standards for evidence and argument are critical in a competitive system where competing groups have palpable incentives to withhold information, monger stilted information, use irrelevant information, or use any tactic to win. In the following paragraphs, I etch out my argument using US government as an example.

Epistemic standards in government

Differing epistemic standards pervade different branches of the US government. The epistemic standards are loosely correlated with the idealized ‘expected’ function of the branch of government.

Let me begin by outlining the differing epistemic standards and then I will go into detail as to the possible effects of those ’standards’ or lack thereof.

Epistemic standards in judiciary

US judicial system uses the adversarial system in which each of the parties present its case to a neutral party (judge or jury). Each side is supposed to furnish evidence in support of its argument, and an ‘impartial’ judge decides on what evidence is better in terms of its applicability and strength.

The adversarial system is a competitive system that relies on the sparring parties to furnish evidence. Like any competitive system, the sparring parties have explicit incentives to withhold information from each other and misrepresent information. The system relies on the ‘other’ party to excavate any such violations, and sometimes on the neutral party. There are some other formal procedures to limit the kind of evidence that can be presented (though some are rooted in alternate theories) and procedures for sharing corroborative evidence. There are also formal procedures as to what kind of arguments can be presented.

The adversarial judicial process inarguably uses the strictest standards of evidence amongst any branch of government.

Epistemic standards in Legislative and Executive branch

While the legislative process is largely a ‘competitive’ system, it has no formal epistemic standards limiting the kind of evidence or arguments that can be presented. The strength of the evidence presented, its applicability, etc. are either ‘judged’ by ‘citizens’ (substantially mediated by media) or by members of the other competing party.

The problem with legislative branch is not only that it is a competitive system but that is a corrupt, special interest driven, competitive system. The system provides little incentive to the members to judge the evidence in an impartial manner with the ‘nation’s’ best interests in mind.

There are literally no epistemic standards that hold back the executive branch except for some loose constraints that tie those standards to marketability of a particular policy decision.

Side note: Adversarial systems flirt with Inquisitorial systems

Congress also uses the ‘Inquisitorial system’ when it conducts ‘Congressional Hearings’ to ‘investigate’ a particular issue. Of course due to partisanship pressures, the inquisitorial system often uncomfortably borders on ‘inquisition’.

Conclusion

Lack of epistemic standards for evidence and argumentation hobble the democratic system immensely. One way to correct the problem would be to create governance structures that explicitly involve independent bodies that judge the strength and applicability of evidence presented.

Epistemology of Causality

How do we know that something is the ’cause’ of something and how do we impute ‘causality’ through data?

To impute causality in quantitative models, we rely on the argument that it is unlikely that the change in Y could be explained by anything else other than X since we have ’statistically controlled for other variables’. We ‘control’ for variables via experiments or we can do it via regression equations. This allows us to isolate the effect of say variable x on y. There are of course some caveats and some assumptions that go along with using these methods but robust experimental designs still allow us to impute causality in a fairly robust way. Generally the causal claim is buffeted with description of a plausible causal pathway. All of the analysis and the resulting benefits of reliably imputing causality are predicated on our ability to ‘correctly’ assign numbers to ‘constructs’ (the real variables of interest).

Let’s analyze now how qualitative methods can impute causality. While it seems reasonable to assume that ’systematic’ ‘qualitative’ analysis of a problem can provide us with a variety of causal explanations and under most circumstances provide us with a reasonably good idea of how much each of the explanatory variables affects the dependent variable, there are crucial problems and limitations that may induce bias in the analyses. Additionally, we must define what constitutes as ’systematic’ analysis.

Another thing to keep in mind is that ethics and rigor are not enough to impute causality. What one needs are the right epistemic tools.

A lot of qualitative research is marred by the fact that it ’selects on the dependent variable’. In other words it sees a dependent variable and then goes sleuthing for the possible causal mechanisms. It is hard in that case to impute wider causality between variables because the relationship hasn’t been tested for varying levels of X and Y. It is useful to keep in mind that sometimes it is all that we can hope to achieve. Additional problems can emerge from things like “selection bias” and logical fallacies like “Post hoc ergo propter hoc”. Partly the way qualitative research is written can also impose its own demands and biases including demands for narrative consistency.

It is unclear to me whether a system exists to impute causality reliably using qualitative methods. There are however some techniques that qualitative methods can borrow from quantitative methods to improve any causal claims that they may be inclined to make – one is to use a representative set of variables, the other is to look for ‘natural experiments’, and pay attention to larger sociological issues and iterate through why alternative explanations don’t apply as well here – a sort of a verbal regression equation.

There are of course instances where deeper more in depth analysis of few cases allows one to get a deeper understanding of the issue but that shouldn’t be mistaken as coming up with causes.

Epistemology of generalization in empirical methods

There is very little space that we get edge ways when we think about a systematic theory of generalization for empirical theories unless. To generalize we must either ‘know’ fundamental causal mechanisms and how they work under a variety of contextual factors or use probability sampling. Probability sampling theories are built on the belief that we know nothing about the world. Hence we need to take care to collect data (which ideally transposes to the constructs) in a way that makes it generalizable to the entire population of interest.

Causal arguments in Qualitative research

For making ‘well grounded’ causal arguments in qualitative research - say with a small n - the case must be made for generalizability of the selected cases, use deduction to articulate possible causal pathways, and then bring them together in a ‘verbal regression equation’ and analyze which of the causal pathways are important - as in likely or have a large effect size- and which are not.

Epistemic standards in interpretation and methodology

Quantitative methods share a broad repertoire of skills that is shared across the disciplines while comparatively no such common epistemic standards exist across variety of qualitative sub-streams that differ radically in terms of what data to look at and how to interpret the data. Common epistemic standards allow for research to be challenged in a variety of ways. From Gay and Lesbian studies to Feminist Scholarship to others – there is little in common in terms of epistemic standards and how best to interpret things. What we then have is merely incommensurability. Partly of course different questions are being asked but even when same questions are being asked – there appears to be little consensus as to what explanation is preferred over the other. While each new way to “interpret” facts in some ways does expand our understanding of the social phenomena, given the incommensurability in epistemic standards –we cannot bring all of them to a qualitative ‘verbal regression equation’ (my term) through which we can reliably infer the size of the effect of each.

Caveat
The above article deals with the debate between qualitative methods and quantitative methods on a small select sample of issues - generalizability and causality - that are explicitly more tractable through quantitative models. It would be unwise to construe larger points about relevance of qualitative methods from the article.

Social phenomenon, unlike ‘natural science phenomenon’, is bound and morphed not only by nature (evolution etc.) but also history, institutions (religious, governance etc.), and technology, among others. Before I go any further, I would like to issue a caveat to point out that the categories that I mention above are not orthogonal and in fact do trespass into each other regularly. We can study particular social phenomenon in aggregate through disciplines like political science, which study everything from study of psychology to institutions to history, or study them by focusing on one particular aspect – psychology or genetics – and investigating how each effect multiple social phenomenon like politics, communication etc.

Given the disparate range of fields that try to understand the social phenomenon, often times the field is straddled with multiple competing paradigms and multiple theories within or across those paradigms with little or no objective criteria on which the theories can be judged. This is not to say that theories are always mutually irreconcilable for often times they are not (though they may be seen as such - which is an artifact of how they are sold), or that favoring one theory automatically implies rejecting others. Success of a theory, hence, often times depend on how well it is sold and the historical proclivities of the age.

Proclivities of an age; theories of an age

Popular paradigms emerge over time and then are discarded for entirely new ones. It is not that the old don’t hold but just that the new ones hold the imagination of the age. Take for example variables that people have chosen to describe culture over the ages - Weber argued religion was culture, Marx argued that political economy was culture, Freud proposed a psycho-analytical take on culture (puritan, liberated etc.), Carey proposed communication as culture, political theorists have argued institutions as culture, bio-evolutionarists argue that cognition and bio-rootedness are primary determinants of culture, Tech. evangelists have argued technology is culture, while others have argued that infrastructure dictates culture.

It is useful to acknowledge that the popularity of the paradigms that were used to define culture had something to do with the most important forces shaping culture at that particular time. For example, it is quite reasonable to imagine that Marx’s paradigm was a useful one for explaining the industrial society (in fact it continues to be useful), while Carey’s paradigm was useful to explain the results of rapid multiplication (and accessibility) of communication (mass-) media. I would like to reissue this caveat that adopting new paradigms doesn’t automatically imply rejecting the prior ones. In fact intersection of old and new paradigms provide fecund breeding grounds for interesting arguments and theories – for example political economy of mass media and its impact. Let me illuminate the point with another example from Political Science which a decade or so ago saw resurgence of cultural theory at the back of Huntington’s theory of ‘Clash of civilizations’. Huntington’s theory didn’t mean an end to traditional paradigms like economic competition; it just postulated that there was another significant variable that needed to be factored in the discourse.

The structure of scientific revolutions

Drawing extensively from historical evidence from the natural sciences, Thomas Kuhn, a Harvard physicist, argued in his seminal book, The Structure of scientific revolutions, that science progressed through “paradigm shifts”. While natural sciences paid scant attention to the book, the book provoked an existentialist crisis within the social sciences. To arrive at that crisis point, social scientists made a number of significant leaps (not empirically based) from what Kuhn said – they argued that growth of social science was anarchic, its judgments historically situated and never objective, and hence the social sciences were pointless – or more correctly had a point but were misguided. This self-flagellation is typical in social sciences that have always been more introspective about their role and value in society as compared to the natural sciences, which have always proceeded with the implicit assumption that ‘progress’ cannot be checked and eventually what they produce are merely tools in service of humanity. Of course, that is quite bunk and has been exposed as such without making even the slightest dent in the research in science and technology. Criticizing natural sciences, especially the majority of it that is in service of ‘value free’ economics, doesn’t take away from the questions that Kuhn posed for the social sciences. Social scientists, in my estimation, put disproportionate emphasis on Kuhn’s work. Social science is admittedly much behind in terms of coming up with generalizable theories but they have been quite successful in identifying macro-variables and phenomena.

The most intractable problem that social scientists need to deal with is answering what is the purpose of their discipline. Is it to describe reality or to critique it or engineer alternative realities? If indeed it is all of above, and I believe it is, then social science must think about melding its often disparate traditions – theory and practice.

Rorty and the structure of philosophical revolutions

Richard Rorty in his book, Philosophy and the Mirror of Nature, launches a devastating attack on philosophy – especially its claims to any foundational insights. Rorty traces the history of philosophy and finds that the discipline is embedded, much more deeply than social science, in the milieu of paradigm shifts – philosophers from different ages not only offer different “foundational” insights but often times deal with different problems altogether.

Battling at the margins

Those who argue that the singular purpose of social science should be to normatively critique it and offer alternative paradigms are delusional. Understanding how a society works (or how institutions work, people work) is important to craft interventions – be it drug policy or engineering new governance systems. Normative debates often times are nothing but frivolous debates at the margins. The broad overarching problems of today don’t need normative theorists devoted to analysis - though I don’t dispute their contribution - they are evident and abundantly clear. When we take out the vast middle of what needs to be decided, normative theory becomes a battle at the margins.

Post-positivist theorizing; and the sociology of research

The most significant challenges for social science as discipline lie within the realm of how the discipline aggregates research and moves forward and how that process is muzzled by a variety of factors.
Imre Lakatos sees “history of science in terms of a continuous competition between alternative research programs rather than of successive conjectures and refutations on the one hand, or of total paradigm-switches on the other.” Lakatos argues that any research program possess a kernel of theoretical principles which are taken as fixed and hence create a ‘negative heuristic’ that forbids release of anomalous results, and instead scientists are directed to create a “protective belt” of auxiliary assumptions intended to secure correctness of theoretical principles at the core. Finally, ‘positive heuristic’ is at work to “Defend and extend!” (Little, 1981)

Post-positivist scientific philosophy, like the ones forwarded by Kuhn and Lakatos, raise larger questions about the nature (and viability) of the scientific enterprise. While we may have a firmer grasp of what we mean by a good scientific theory, we are still floundering when it comes to creating an ecosystem that foments good social science and creates a rational and progressive research agenda. (Little, 1981) We must analyze the sociology, and political economy of journal publication as the whole venture is increasingly institutionalized and as careerism etc. become more pronounced.

“Communication” comes from the Latin word communicare, which means “to make common”. We communicate not only to transfer information but also to establish and reaffirm identities, mores, and meanings. The two major localities where “communication” takes place are the consumption of mass media, and everyday conversation. While both inform how we view the world, and what is considered important, scant attention has been paid to understanding the nature and shape of everyday communication and charting its impact.

In the entire realm of what is considered communication, arguably the most important part is the “everyday conversation” – the repeated mundane conversation. I say this not because “everyday conversation” occupies the most time, for admittedly consuming mass media does that, but because “everyday conversation” is still the primary site where people seek approval. While the motivations for entering into a conversation have remained largely the same, the nature of everyday conversation has changed dramatically over the last century. Firstly, today the conversation is carried out between socially competitive peers rather than empathetic family members, and secondly the things that provide value, or things that people seek approval on, have changed from “being a good son or daughter or some other social relation” to fickle, competitive identity markets based on consumption of commercial products (or related training like cooking shows, home improvement shows, travel shows) and entertainment. In other words, with increasing atomization and resulting heightened anxieties about identity, for we no longer get most of our identity from family or some other archaic system, but through consuming the right kind of entertainment and consuming appropriate products, everyday conversations have effectively become negotiations of cultural identity among social or (generally “and”) economic equals.

The negotiation of commercialized cultural identities is done via issues like sports, movies, and other cultural products while contentious topics like politics, religion, and race with little or no commercial value are frowned upon as conversation topics. The key ideal in conversation is politeness (and conformity) and it is just not polite to bring in contentious topics except to mention harmonious approval, cues for which may have been exchanged before.

Given that the motivation for everyday conversation is garnering social approval, attention is paid to story telling, artful handling of anecdotes, sarcasm etc. and not on “accurate” objective reasons. Additionally, the exchange about product preferences is liable to be subjective, and hence not eligible for closer scrutiny, and anchored to some accepted commercial shtick or parameters of “coolness” or “hipness”. This ineligibility for closer scrutiny is there for a reason for it is in the protection of that kernel of ‘irrationality’ and some vague notion of ‘individuality’ can one sell absolutely anything. The fact is that trillions of dollars in this economy rides on the fact that tomorrow millions of people will wake up and make a suboptimal decision - or more accurately be convinced about their economically sub-optimal decisions – about their decision to buy some product.

The other important facet of everyday conversation, as I mentioned earlier, is that it is done primarily between economic and social equals. Conversation between classes has altogether dried up. This drying up can be seen as a result of drying up of places where these interactions used to take place. Cross class interaction or conversations always took place when the person from a lower class offered a service to the person from the higher class. The fora for these exchanges of anecdotes and stories between economic classes have almost dried up under current economic regime. For example, the mom and pop stores manned by neighborhood people have been replaced by chain stores that hire salaried employees with high turnover and whose only focus is to provide an efficient economic transaction and offer an empty courtesy. These routine commercial interpersonal transactions not only keep us from learning the difficulties across classes and hence possibly build empathy, but also have a profound impact on our everyday interaction with other people- even of similar social status. Let me weave in another anecdote here to illustrate the point. When I first came into this country, I was often asked some variation of “how I was doing?” at the beginning of each conversation. I frequently responded by providing full descriptions of how I was doing. It was only after many months and after receiving numerous impatient glances that it dawned on me that people expected nothing but empty curtsies.

The normative point that I want to make is that our everyday conversation affects the nature and extent of our knowledge and style of argumentation. For example, it affects whether one is interested in politics or not, and the political proclivities one may have. The site of “everyday conversation” needs to be reclaimed to build a healthy body politic. Specifically for politics, we may need revival of public conversational spaces what Habermas writes about and what Tocqueville observed.

It doesn’t take long for a person to realize that the current democratic model is deeply flawed. The continued failure of about thirty percent of Americans to realize that Iraq did not have weapons of mass destruction speaks volumes of the limitations of the current information stream and the democratic system based on it. As our democratic stands now, it works, or more accurately doesn’t work, in the following way – it needs three years of continual coverage that the war is going catastrophically for about 70% of the citizens to finally realize that it is indeed going badly. In other words, the current democratic model not only has a substantial time lag in information dispersal (and hopefully action) but also a model that doesn’t respond to gradual increases in problems like gradual increase in poverty. In other words, it is a ‘frog in the hot water’ (oblivious of the gradual rise in temperature) model. And while we respond to pointless scandals and excel at slaying imaginary ghosts, we can build little momentum towards solving some of the most exigent problems in an optimal way. I argue that the current state of democracy has a lot to do with its modern origins that were based on that period’s exigencies and the then prevailing wisdom (Adam Smith).

The modern origins of democracy, that typically begin with democratic US, point to a system formed in response to elite and colonial excesses. The chief worry at the time was to prevent the exercise of power by a small minority with no vested stake in the welfare of the masses. Hence, appropriately, the system of democracy that was formed as a result of it was tailored towards distributing power to common citizens and hence in turn maximizing the legitimacy of the decisions made. Critically, since the British excelled at monopoly, ‘founding fathers’ (themselves rich) strove to institute Capitalist attitudes towards trade, private ownership, and business.

Modern democracy was never geared towards coming up with the ‘best’ decision or maximizing some other utility function. To analyze democracy’s claims to making ‘best’ decisions, one has to make a number of leaps including that every citizen is aware of his self interests and larger public’s interests; each citizen forcefully hawks his or her ideas in the marketplace of ideas, and that the best information and best arguments will win in this marketplace and form the basis for legislation. In other words, claims to the normative superiority of democracy it seems come from a reasonably well functioning market of ideas - a market that is not driven by the most saleable or seductive ideas but by the ‘best’ ideas (which it hopes would sell the most). This in turn seems like a particularly botched hypothesis in a market with pervasive ignorance, as Converse et al. have shown.

The concept of an idea marketplace deserves further attention for that is from where all possible benefits of democracy are actually supposed to accrue. The fact is that while a lot of theoretical energy in the field of democratic theory has been tailored towards justifying the moral superiority of democracy over other systems, an ailment that I believe can be traced to Cold War days, there has been little focus on critiquing the fundamentals of democracy. If look at the time period just before Cold War, there was a lot of intellectually energy invested in analyzing whether having a Capitalist economic system puts at risk the functioning of the marketplace of ideas. There is little doubt in my mind that if profiteering is the guiding principle of information distribution, let alone the entire society, it seems unlikely that good information, a requisite for the marketplace of ideas and citizenship, will flow unpolluted. The idea that the market can let alone decide and asses an accurate value on each piece of information and give to the citizen at the appropriate time in appropriate manner is ludicrous at best. It comes as no surprise to me that economic market has increasingly usurped the democratic marketplace of ideas. A prime exemplar of the usurpation is the proclamation that head of Ford once made when he said, “What’s good for Ford is good for America.”

There are two points that one can glean from the above discussion – one is that there is little doubt that the current democratic system is fatally flawed and its flaws primarily stem from a stilted realization of the marketplace of ideas. If we indeed want to continue with some form of governance that takes into account public opinion, we must strive to make the public more informed about issues. To the extent that people can be made more informed by instituting reforms in media, we must do so. Alternatively we can try to come up with better decision making models that provide better incentives to citizens to be informed and for lawmakers to aggregate the choices with less pressure from lobbyists. Deliberative polling model, which takes a random representative sample of the populace and lets them deliberate about issues, does just that. But it fails to fix the wider malaise that afflicts the wider body politic. It is likely that a combination of the above two methods presents us with the best chance of succeeding as a democracy.

Dr. Victor Stenger is professor emeritus of physics and astronomy with University of Hawaii and adjunct professor of philosophy at the University of Colorado. He is the author of God: The Failed Hypothesis. How Science Shows That God Does Not Exist, which debuted on the New York Times Bestseller List last week.

Q. When did you first realize that you were an atheist? Was it a sort of a Eureka moment or a gradual realization?

In high school I started reading a lot of popular science, especially astronomy, nuclear physics, and evolution. I began to see Catholicism as irrational but I did not become an atheist immediately. When I was a graduate student at UCLA I attended a Methodist church and sang in the choir. When I lived in Hawaii, my wife and I sent our kids to church-related schools, although we did not go to church. Finally, in the 1980s I began to get involved with the skeptical movement and learned about Humanism. The more I gained from experience, the more I read, the more I realized that the God concept had no merit.

Q. Church attendance and belief in God have remained relatively steady in US, while there has been a precipitous decline in Western Europe. What do you think is behind this?

Big money is given by extremely conservative, wealthy sources in the US to churches and other organizations such as so-called think tanks to brainwash Americans. Europe is less vulnerable to what Chris Hedges, in his best-seller, called “American Fascists”. Incidentally, he is not an atheist.

Q. On occasion when I chance upon religious programming on TV – it seems half gimmickry and half psychological therapy. In fact, mass religions from fairly early on took on the job of providing ‘guidance’ to people. What do you make of this sort of role of religion?

While it is mostly good-intentioned, much in the religious right — the American<br />Fascist movement — is motivated by the desire for political power and the helping people aspect is a phony con game that is part of the scheme.

Q. This question is somewhat related to the previous one. Say if we were to find out that belief in God is psychologically helpful, can we argue for an evolutionary reason behind existence of religion? This question was famously asked by Time in its article – The God Gene – does our DNA compel us to seek a higher power? What do you make of these kinds of assertions?

I don’t think it’s in the genes. I think religion evolved in cultures, ironically, by Darwinian means. Religion has always be the method used by those in power to keep the masses in line. For example, I was recently in India. There the vast majority of people live in misery and squalor. But they don’t complain, they don’t revolt against the rich, because the Hindu religion tells them it is their dharma - their fate. In the West, the divine right of kings justified their dominance. Today George Bush tells us that he is doing God’s work.

Q. People have often times argued that religion is needed to uphold moral values. Psychology literature points to that people are more liable to take advice from institutions or people they trust. Is there a case to be made for religion to be there as a service that disseminates morality?

This is the prime example of how religious brainwashing works. People are told that morals come from God. But the facts say otherwise. Moral concepts such as the Golden Rule were around centuries before Jesus. They are the collective principles of humanity. Studies show that atheists are at least as moral as theists, and certainly there is a connection between fundamentalism, in Islam and Christianity, and antisocial behavior. I prefer to call myself a humanist rather than an atheist because Humanism is the source of our morality and provides a positive outlook on life.

Q. Religion in everyday life is understood as something uncontestable while scientific theories are considered debatable. How can we provide a more open attitude towards investigating religion?

Religion makes testable claims so these can be treated the same as any scientific claim. I document these in detail in the book, but let me give you one example. Most believers do a lot of praying and think it has a positive effect. These effects should be observable. Controlled experiments have been done and have found no effects. It could have turned out otherwise, in which case I would have to admit that science had found God.

Q. It is a well known fact that very few people actually ever read the religious texts and it is likely that very few of those who read them understand them. So there is chasm between the way a religion is lived and the way it was fundamentally conceived and hence the numerous ‘fundamentalist’ movements. The argument that I am making is that ‘faith’ that is driving most religious people is of a vague though absolute kind. Debunking the extraordinary stories of the books, and even providing convincing arguments against God is unlikely to change the views of the majority of religious people.

Probably. But there are still a lot of people I think I can reach with rational arguments: agnostics; believers who are not too sure; young people, especially college students who are learning to think critically and have not yet formed their views. Also I provide ammunition for those who think like I do to use in their arguments with believers.

Q. Science thrives on the parsimonious model. One shouldn’t create something if it isn’t needed to explain the phenomenon at hand. Hence if all ‘natural’ phenomena can be conceivably explained by variables at hand then why devise new ones. This, I believe, is one of the chief arguments that you try to make about absence of God. Can you expand a little more on this?

In an earlier book, Has Science Found God? I refute the claims that there is scientific evidence for God. In this book I go much further than just the absence of evidence argument that you reiterate in your question. I claim there is positive scientific evidence against the existence of the God most people worship, as in the absence of support for the efficacy of prayer that I mentioned earlier.

Q. It is a well-known scientific corollary that absence of proof is not proof of absence. The kinds of models that you describe in your book are really a probabilistic debunking that derive their strengths from 95% confidence intervals and unlikelihood of the hypothesis but not proof that it doesn’t exist. Can you shed a bit more light on this?

The word “proof” has at least two different meanings. In logic and mathematics, a proof or disproof is with certainty given the starting assumptions. In science and law, proof means beyond a reasonable doubt. The latter allows one to conclude that God can be “proved” not to exist if the data show this beyond a reasonable doubt. Note I use “show” rather than “prove” in the subtitle to avoid that confusion.

Q. One of the arguments that is made by people who believe in God is that there must a reason to our existence. This is sort of an existentialist argument that says that we must have a cause behind our lives and only human lives, I may add. What do you think of this?

That is simply a pious hope. There is no basis, and I would add, no evidence, for this. In fact, the universe looks just as it should be expected to look if there is no special role or purpose for humanity. However, it is important for me to add that this does not mean that we cannot find purpose in our own lives in family, work, art, music, doing good deeds, and so on. In fact, releasing the bonds of religion gives us more freedom to explore all that life has to offer.

Q. Tell us a little more about the kind of problems you see if we allow religious superstition to dictate policy and even science.

As documented in several other books, the religion-based decision making of the Republicans and Bush administration does more harm than good, threatens the health and well being of all of us, and increases the amount of unnecessary human suffering in the world. For example, most of the federal money spent on AIDS, in Africa and America, goes to advocating abstinence and none to condoms. Scientific studies showing that abstinence does not work are deleted from government reports.

Q. Any final words to the believers and the non-believers?

If scientific evidence were ever found for God or some other form of the supernatural, then scientists like myself would become believers. I give hypothetical examples of observations that would convince me that God exists. I ask all believers and nonbelievers to look at the data and argue about it rationally, without polemics or ad hominems. I try to do this in my book.

It is often times much harder to evaluate how much we know and don’t know about everyday life as compared to say ’scientific facts’ for which we exactly know whether we know them or not know them. We all live with some understanding of the etiology of social phenomenon, or produce one when confronted with a question. Hence we instinctively feel that we know the nature, extent, and cause behind social phenomena. Our understanding of the world, and knowledge, is based on a wealth of information that we glean from living in the world. Our knowledge though is biased in one significant way – it is limited to the environment that we live in, and to the knowledge of outside world that modern communication technologies like TV, phone, books etc. make available to us.

The case of the climatologist

It is troubling when natural scientists have the same attitudes towards social scientists that they accuse the lay public of having towards them.

A prominent professor of Communication recently narrated this anecdote to me – “I was at the meeting for American Association for the Advancement of Science this past weekend. The meeting used to be dominated by natural scientist, and still is, though recently social scientists have been given more attention.

Anyways, this time the meeting was dominated by the topic of climate change. On Saturday night, I had the good fortune of having dinner with a group of climatologists who spend time complaining about how people just don’t get what they were saying. Some person sitting in his bedroom watching television says that ‘I don’t see any warming – last winter was still cold. I just don’t buy it’. The climatologists were clearly frustrated by this attitude, “Leave the science to us. If we tell you that something is going on- believe it. Just don’t come back and tell us that since you don’t notice something – it doesn’t exist.”

The conversation went on like this for long time and then the same climate researcher, who until then was expressing his frustration with public’s inability to understand science, stuck a more positive note saying, “Americans’ attitudes towards climate change have really changed over the past year. I think they now seem to be getting the message.” After some nodding from others in the group, the conversation eventually wended its way towards me as a scientist asked my opinion –”Aren’t you into communication or something. You must have something to say about this.” As a matter of fact, I did, given my own extensive research and interest in the area of communicating information about climate change. I said that I don’t think data supports such optimism- American public’s attitudes haven’t changed much on either the question of whether climate change exists, or for those who believe it exists on whether it has anything to do with humans. This climate scientist looked at me as if I was some sort of a moron who didn’t really know what was happening. He then proceeded to argue that I didn’t really know what I was talking about and the perception shift in American public’s mood was real and significant.

I was just kind of stuck by their attitude – it seemed to mimic the kind of attitudes they criticized in lay people, or the arm chair arbiters of data on climate change. The fact of the matter is that climatologists have little or no insight into people’s attitudes or attitude change. Their own attitudes are based on anecdotal evidence or convenient samples of their cohort rather than random sample surveys and yet they felt confident talking as if they knew all the facts.”

Stop for a second and ponder about what you know and what can be known. Think about say what you know about people’s support for Bush. There is a good chance that if you are a typical American, you have mostly either met Bush supporters or people who oppose Bush. Do you think that you can really reliably extend this anecdotal evidence to explain attitudes of 300 million people? The fact of the matter is that whatever idea you have a number of Bush supporters is either through what you know from the results of elections or the more or less scientifically conducted media polls. If you take that information away, you have little to judge the support of Bush by.

It may come as a surprise to you, but if you investigate carefully, about how little we actually know about the nature, extent, and cause of social phenomenon. It is very hard to look outside oneself – to think of a person who thinks differently, is equally convinced of the merits of his opinions etc. By either believing that others think like us or adopting social attitudes or ‘folk knowledge’ as truth, we continue to be uninformed.

The case of the cousin

A couple of years I was at my cousin’s family for a dinner. After the dinner we were sitting in the dining room when the conversation drifted towards poverty and the causes for poverty. My cousin, who comes from a well off family and was studying to be a doctor at the time, kept citing Oprah as ‘proof’ that the only thing holding poor people back was themselves. Eventually I got tired of her delusional argument and noted acerbically that she didn’t know what she was talking about. I said that I don’t even think that her opinion was valid and worthy of any attention. She took this, rightly, as an insult and said that every opinion was valid. I said that I disagree with this assessment. Since she was studying medicine at the time, I asked her how she would feel if I told her that I believed blood letting is a great way to cure cancer. She never answered whether she would consider my thoughts on medicine as valid opinions but kept to her guns arguing people’s opinions should be respected. I agree to the premise but of course I don’t buy the assertion every opinion is equally valid. Clearly arguments based on better data, and using better normative groundings are more valid than ones using anecdotal data with limited normative basis.

Anyways, I believe that my cousin was on to something like that climate scientist. I will use that to expand my thesis to the rest of humanity and not just natural scientists. There are normative issues and empirical issues in any social science debate. By normative issues, I mean moral issues or judgments about right and wrong and of course there is robust social science and philosophy around deciding what is right and wrong but at the heart of this kind of moral judgment people believe they have the right to their opinions. The other important component is the empirical part, testable items around which we have evidence. There is also validity of argumentation based on the way one cites data, the quality of data, logic, issue that is addressed, etc. Anyways, this second part is what average people typically completely botch. I am sure my cousin wants poor people to live comfortably, and I am sure the climate scientist wants good data on public attitudes towards climate change. With normative issues out of the way, social science provides ways (imperfect ways) to analyze causes (and hence find interventions) around poverty, and calculate any attitude change around global warming in Americans.

Let me summarize the arguments that I make above. Firstly, there is very little that can be reliably discerned from immediate environment about wider social phenomenon, and popularity of certain attitudes and ‘facts’. Similarly, there is little reason to believe that we can come up with a useful understanding of causation (and hence draft interventions or not draft any) about the wider social phenomenon. In fact it is hard to imagine that most people would have a good understanding of person level phenomenon, though they may rely upon them on a daily basis.

The second point that I want to make here is that argumentation based on anecdotal evidence has less validity than one based on better understanding of facts. We can always come up with convincing arguments that rely on anecdotal evidence, and the reason why they are convincing is because they tap into other people’s reserves of prejudice and ‘folk knowledge’, but they tend to be of little use if our real interest is in understanding and not winning a narrow debate or an electoral contest. Only facts or good social science can serve as basis for substantive ethical argumentation, which I believe is the only way to reach a better understanding of truth.

Lastly, what I am asking people to do is be more reflective and be more humble in accepting what they don’t know about social phenomenon and that includes social scientists.

There is a schism that runs right in the middle of social science divvying up the field between the critical theorists and the positivists. Positivists aspire to model the success in nature science and hence tend to focus on the causality and statistical proof, according to Dr. Tang at the National Taiwan University. Critical school, on the other hand, starts from some starts from some philosophical axioms, for example – “social good” or “justice for all” or “maximizing social good”.

The approach by “critical theorists” is riven with difficulties due to the multiplicity of the philosophical starting points around which one can build theories. As one would expect, critical theory today has myriad “schools”, each based on different philosophical assumptions and each with, if one may say, their own geometry and calculus which works only in their own universe. Positivists circumvent the epistemological and other philosophical issues that dog the critical theorists by relying on the natural science model of doing research that stresses on coming up with falsifiable hypothesis that can then tested either via experimentation or observation (within representative randomized sample). Given the difficulty of defining and measuring useful variables in social science, positivists rely upon their own methodological axioms though a lot of research is currently underway to help refine methodology.

The dichotomy in the field brings one to question how social science should ideally be conducted. The answer depends on what one expects from social science. One may argue that social science has ceded its primary responsibility of trying to resolve the dispute between differing philosophical paradigms and wrestling with issues pertaining to the nature and future of society. Without the moral or philosophical grounding, a lot of research may seem like monkey work – repetitive and commercial applications aside utterly aimless. On the other hand one may argue that quantitative work often illuminates how humans and society works and it is first important to understand both of them before we move on to the task of circumscribing their behavior in philosophy. I would argue that social science’s aims need to be a hybrid of both of the strands. Social science needs to continue to grasp with the important epistemological and philosophical questions that underpin our existence and provide direction in a way to where research is headed. At the same time, social science needs to be more pro-active in understanding humans and society.

A couple of years ago Dr. Muhchyun Tang wrote an interesting article about how Internet is changing museums. In particular, he argued that today people have a variety of ways to explore a collection via Internet as opposed to carefully orchestrated explorations in a brick and mortar museum with a curated exhibition.

A curator comes up with a story along with other contextual information about the exhibit and arranges the exhibition so that the person exploring it has only a few chosen entry points and few ways of exploring the collection. Some of the impediments are put in deliberately while others are a result of hosting an exhibition in the real world where the design of building etc. still matter.

Cut to the online world and the user is untethered from most of curated connivances. This in turn maybe a result of the fact that people haven’t really understood how best to present a virtual museum but that is not the point I want to get into. The result of the untethered experience is that these cultural objects are seen in a twice removed setting -e.g. a pot taken from an archaeological site and then photographed and put on the Internet. So what is the result of all this? It is hard to give an objective listing but one can see that some of the “meaning” is lost in this journey of an artifact from the ground to the Internet.

This article led me to a larger question - what happens when information that was once tethered in a context or a story is made available virtually free of context over say Google. Is storing information in hierarchical networks or associations obsolete? How do you maintain integrity of information when context-free snippets of information are freely available?

Say of example - once upon a time people learned about history via a scholar who chose carefully the specific issues about history. Today, a teen gets his/her history by searching on the web often encountering a lot of miscellaneous information. I would argue that the person then can come away, from such a scattered exploration, with a bunch of miscellaneous trivia and no real understanding of the major issue at hand. The key idea here is that for transmission of “knowledge” - the integrity of information is of prime value.

Rationalism is not an end in itself but a tool which helps us “understand” something. Merriam Webster defines the word “understand” as “to grasp the meaning of” or “to grasp the reasonableness of”. The fact is that modern “understanding” is closely tied to agreeing to the reason/meaning behind something. Going beyond this initial premise we can see that the whole structure of modern perception/conception is based on attributing meaning to things. Most of social science has been devoted to understanding the structure, shape, evolution and construction of this meaning and its effects of us who live in this “meaningful” world. Mining the ontology of “meaning” is a circular task because ontology itself is defined in rational terms.

World also has its “unreasonable” people - the radical, the fanatics, the jihadists or more simply the believers. The fact that we can understand something itself is seen as a conceited concept - especially in eyes of people who “believe”. These believers are further split in two branches - one who believe that the world is too complex to be understood by human intelligence and the ones who believe there is no meaning in the world.

Scientists have proven time and again - ironically through rigorous scientific experiments - that humans are fairly irrational, that we learn primarily through mimesis, we are endlessly ritualistic and that subtle change in wording/environment can lead to vastly different results. Let me give you an example - we work better under deadlines. We may know that such and such is beneficial for us but we may endlessly procrastinate simply because we can. A “rational” person may not need an additional incentive to do a thing which is beneficial for us.

Let me get back to the whys and wherefores of this piece. Logic has its genesis in Maths and in the mathematical world things are absolute i.e. arguments don’t demur to variety of view points unlike “real” world. Logic, therefore, makes a particularly poor transition to the human world. The point that I am trying to make is that reasoning is not an end in itself but a “power tool” -literally and figuratively- that can help us communicate and “understand” and even empathise. Contrarily reason as an arbiter between people has its own limitations - primarily that it can only work where people have compatible moral/other standards.

Let me end by pointing out the egregious transition that I made from reason to rationalism to logic. All three are different. Reason doesn’t need to be “reasonable” but rationalism is based on the “reasonable” clause. Reductionism logic -the variety we use most commonly - starts from a basic assumption - axiom or postulate- and it can be a Bible or a Koran or a science text book. Another flaw in reason is that it doesn’t have to be “true”. Rationalism on the other hand is based on the assumption that we have complete knowledge -which we really don’t have. Alright I am being unfair. we don’t really need all knowledge but just the major variables for example I am hungry is a good enough reason for eating.

The larger problem though is that reason is very much goal oriented as in we often “reason” to “prove” our side of the story or our morality and the fact that the strongest reasons for supporting something are increasingly the ones that are hyped most and that tap into our hidden prejudices for example most Americans don’t like to give welfare to poor people as it exploits the common myth that welfare recipients are lazy black people.

This brings me to the discussion of “asymmetric nature of knowledge”. Focualt argues that the universe of knowledge is asymmetric and power centric. Some pieces of knowledge - let me tap into say the case of Muslim scarf - get prominence while other pieces of knowledge rot in oblivion. So we need to be careful as to what pieces of information we use to weave the “rationale” for say “invasion of Iraq”.

The phrase, “science is not a democracy” has become a favorite with conservative pundits. They often use it to rebuttal arguments like - 99% scientists believe that Global Warming is a real phenomenon.

Well, I am co-opting their slogan to initiate discussion about something else - future of knowledge.

Let us imagine that Google’s model of site ranking trumps all others. Google model relies on the fact that if an information source is reliable, people will “vote” for it with a link. So more the number of links to the site, higher the ranking. There are serious issues with this “democratic” model of information ranking - one of the most perturbing factor among them is that the model is democratic.

How does anyone know the truth if falsehood becomes really popular? One false-hood linked again and again can give it the same credence as a fact. This theory is just an extension of the corner-shop gossip analogy with one substantive difference - in a linked online world, the effects are not localized but global. The model is troubling especially given that more acerbic, vituperative articles very likely get linked more than the dry, measured pieces. To drive home the point, let me etch out a particularly troublesome outcome - a world where all the knowledge is hijacked by zealots of either persuasion.

The democratic idea of knowledge rests upon the twin facts that information choices are diverse enough on the Int