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.

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.

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.

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.

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.

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.

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.

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.

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.

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.