Landemore Democracy as Heuristic the Good Society Final-libre

7/25/2019 Landemore Democracy as Heuristic the Good Society Final-libre

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Democracy as Heuristic: the Ecological Rationality of Political Equality1

Hlne Landemore

Yale University

[email protected]

InDemocratic Reason, I resorted to the metaphor of a group lost in a maze to convey an

important aspect of the problems political communities face when it comes to making decisions

about the common good. The way I described the mazeas involving a series of riddles that

could only be solved by people with very diverse skills unlikely to be consistently found in the

same group of people, let alone the same individualwas meant to illustrate the fact that

including everyone is the best group strategy when faced with complex, ever changing problems

(Landemore 2013: 3-4). Pitting democracy, dictatorship, oligarchy, and a random procedure such

as flipping a coin at every fork in the maze, I argued that the more inclusivedecision-making

processdemocracywas the most likely, on average, to identify the correct answers. The rest

of the book justified this claim by arguing that all things equal otherwise inclusiveness was a

good proxy for the cognitive diversity necessary to the emergence of collective intelligence in

1I would like to thank William Berger, John Weymark, and my research assistant Max Krah, as

well as an anonymous reviewer for the journal, for insightful comments and suggestions on

various parts of the argument. I also owe a special debt to Joshua Miller for his psychological

skills as an editor. Half a month after the deadline for this article had already passed, I realized

the original piece I was trying to finish was not going to work. At that point I was on maternity

leave with a sick child. Somehow, in part by freeing me of all obligations, Joshua managed to

make me write this entirely new piece in record time. This article (whatever its merits) would not

exist without his trust and patience.


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both problem-solving and predictive contexts (see also Hong and Page 2004; Page 2007,

Landemore and Page 2014). In problem-solving (deliberative) contexts in particular, having

more people, all things equal otherwise, increases the likelihood of getting to a good solution.

The metaphor of the maze and the masses allowed me to illustrate the epistemic

superiority of inclusive decision-rules over less inclusive ones. It is indeed obvious in the maze

scenario that many heads are better than one. In pitting democracy against less inclusive

decision-rules, however, I did not make it sufficiently clear why an egalitariandecision-rule, as

opposed to a merely inclusiveone, is the rational answer to the situation. In the way I set up the

contrast between democracy, oligarchy, and dictatorship, I assume that all these rules are

egalitarian with respect to the individuals they include. As a result, the comparison is too

broadly tailored in the sense that I do not consider the possibility, within each rule, of an

inegalitarian weighing of the included voices and votes.

Furthermore, I did not emphasize clearly enough that it is radical uncertainty about the

riddles the group is going to face at each fork in the maze and thus ex ante uncertainty as to the

set of skills to be required, rather than sheer complexity or the distributed nature of the required

knowledge and skills, that makes both inclusiveness and equality of the included a good

epistemic bet over the long run. Some problems might be complex but other might not be. Some

problems may require widely distributed knowledge and skills, other others not. To quote

Keynes, we just dont know (Keynes 1937: 214)!

In this paper, I want to emphasize more than I did in my book the connection between

radical uncertainty, political equality (as opposed to just inclusiveness), and epistemic

performance. I want to make clearer that it is the radical uncertainty that a group faces, both

about the kind of issues they will encounter and about the nature of the knowledge and cognitive


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skills required to address them, which makes democracy as an egalitarian type of inclusive

decision-rule an epistemically superior decision-rule, in particular in comparison to inclusive but

inegalitarian decision-rules (such as hierarchical deliberation and plural voting). In other words, I

now want to account for the superiority of an egalitarianinclusive decision-rule over an

inegalitarianyet inclusive one.

In order to do so, I will offer in what follows a reading of democracy as a smart and

frugal heuristic that groups of human beings rationally resort to in the face of radical

uncertainty. I borrow the vocabulary of democracy as a heuristic from the literature in

psychology and behavorial economics. The first section explains what a heuristic is and why it

can be said to be good or bad only when defined for a particular environment. In the second

section, I defend the assumption of politics as an environment characterized by complexity and,

most importantly, radical uncertainty. The third section presents democracy as a heuristic

composed of two building blocks 1) isegoria or equal right of speech and 2) one person, one

vote and explain why the political equality undergirding each building block is ecologically

rational, drawing a parallel between the principle of political equality and another established

heuristic in the context of financial investment: divide equally or 1/N.

What is a heuristic?

Let me first clarify what a heuristic is. In the now well-established behavioral economics

literature on heuristics and biases, heuristics are presented as deviations from perfect

rationality. They are conceived of as second-best rules of thumbs that can be used in lieu of the

more adequate complex models that we should use under ideal circumstances. Kahneman and

Tversky, pioneers of behavioral economics, thus define heuristics as suboptimal rules that are not


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the best for making decisions but are highly economical and usually effective (1982: 20) given

the limited computational abilities of human beings. Classical examples of heuristics in the

sense of second-best decision rules include the availability heuristicthe common tendency to

make judgments about the probability of events based on the ease with which examples come to

mind. For example, people will tend to overestimate the probability of plane crashes just after

one has made the news. Another example is the anchoring heuristicthe tendency to make an

estimate based on the first information available, even if it is not all that relevant or realistic.

This is a trick on which sellers in the real estate market tend to rely, setting a high initial anchor

in the asking price, which sets the benchmark for negotiations and compared to which any lower

price is bound to seem more reasonable, even if it is not.

The equivalent in the political realm of a heuristic understood in this sense would be the

cognitive shortcuts used by voters in order to make decisions about, say, political candidates for

office. Some heuristics are downright bad, like basing ones decisions solely on a candidates

looks. Some are not as badusing party affiliation for examplebut still yield a crude judgment

of the options. Normatively voters would be better off basing their judgment on a careful

analysis of all the information available about each candidate and their respective platforms.

Here, however, I want to use heuristics in the more positive sense of rules of thumb

that human beings should employ as well as do employ in certain contexts, namely where the

perfect but more complex model simply isnt available, is beyond human cognitive abilities, or

would turn out to yield worse results in the context. I am here following the psychologist Gerg

Gigerenzers use of the term (Gigerenzer 2008). A good example of what he calls fast-and-

frugal heuristic that make us smart, is the gaze heuristic by which outfield players catch

baseballs. Instead of calculating the result of complex equations that physicists would use in


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order to predict the exact trajectory of the ball and its landing point, outfield players use a simple

heuristic that consists of three building blocks:

Fix your gaze on the ball

Start running

Maintain the angle between your gaze and the ball constant.

This heuristic ensures that the player can catch the ball running and contrasts with the physicists

calculus, which aims to predict where the ball will land and would require that the player runs

there and waits to catch the ball standing still.

Heuristics are thus tools for good decisions under resource or time constraints. On this

reading, they are not so much the product of a flawed mental system, although they can be

misleading when not used in their proper context, but rather mentally efficient shortcuts. That is

why it is important to emphasize that the goodness or badness of a rule of thumb depends on the

environment in which it is used. Heuristics need to be used in the context for which they are

adaptive, so to speak. In other words, there are no good or bad heuristics per se, but only

heuristics that are (or are not) ecological (Gigerenzer 2008), or adequate to their environment.

Now that we have clarified what a heuristic is, let me defend the proposition that

democracy can be seen as a good collective heuristic in the particular context of politics. For this

we first need to get a sense of what characterizes politics as an environment for decision-making.

In the next section, I argue that radical uncertainty is an essential feature of politics.

Politics as the sphere of complex and uncertain collective problems

Political theorist since Rawls have emphasized the fact of (reasonable) disagreement as one of

the central feature of politics (see Rawls 1971, 1993, Gutmann and Thompson 1998, Waldron


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1999). I propose that a large part of that disagreement is symptomatic of deeper root causes,

which are not so much deep ontological value pluralism--although some of the fact of

disagreement can perhaps be explained by it--as complexity and uncertainty. Complexity partly

accounts for the fact that even reasonable people, given the various burdens of judgments that

all humans operate under (Rawls 1993), will fail to agree even when more perfectly rational

beings would do so. Complexity is what Rawls emphasizes the most within his notion of

burdens of judgments, which refers to various cognitive limitations, as well as to more

ambiguous causes such as the vagueness of our concepts and the limited social space available to

reconcile values (Rawls 1993: 54-58). But another essential aspect of politics, the one that in my

view accounts for a large part of the fact of disagreement, is uncertainty.

Uncertainty can be defined as the realm of outcomes and associated risks that we are not

aware of. Thus we would be unable to calculate the objective odds of uncertain events even if the

burdens of judgments were lifted. This situation is well captured by Rawls original position--the

thought experiment he uses in earlier work to introduce the two principles of justice a rational

society would in his view settle on (Rawls 1971). This veil is so thick as to render probability

estimations about ones life chances impossible.

Uncertainty is best defined by contrast with both certainty and risk.2Certainty is the

realm of mathematics (2 plus 2 will always equal 4). Setting aside the problem of induction, it is

also, to an imperfect degree, the realm of natural laws (e.g., the sun will rise tomorrow). To an

even more imperfect degree, it is the realm of a very narrow domain of human life and, within it,

politics. Death and taxes aside, we like to think of things like My dog will run to me when I

open the door or Obama will still be President tomorrow as virtually certain.

2See also for a useful distinction between uncertainty, complexity, and difficulty, Scott E. Page

2008.


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Situations of risk, by contrast, are characterized by the fact that although we know what

the outcomes can be, and the probabilities that attach to each outcome, we do not know in

advance which of them will prevail. Risk (or parametric uncertainty) situations include the

situation of the gambler at a casino or the parent wondering what are their chances to have green-

eyed children. Objective probabilities can be assigned to the event. Technically speaking, a

situation of risk is just one of probabilistic certainty.

Uncertainty, by contrast, and as already said, is the situation in which we do not know the

very nature and number of possible outcomes, let alone their respective probabilities. This

foundational distinction between risk and uncertainty can be traced to two iconoclast economists

(Knight 1921: 19-20, Keynes 1921 and 1936; see also Shackle 1955 and 2009). For Knight,

Uncertainty must be taken in a sense radically distinct from the familiar notion of Risk,

from which it has never been properly separated.... The essential fact is that 'risk' means

in some cases a quantity susceptible of measurement, while at other times it is somethingdistinctly not of this character; and there are far-reaching and crucial differences in the

bearings of the phenomena depending on which of the two is really present and

operating.... It will appear that a measurable uncertainty, or 'risk' proper, as we shall use

the term, is so far different from an unmeasurable one that it is not in effect an

uncertainty at all (Knight 1921: 19-20).

Keynes formulates a similar distinction between what he calls 'probability' and 'uncertainty'3:

By "uncertain" knowledge I do not mean merely to distinguish what is known forcertain from what is only probable. The game of roulette is not subject, in this sense, to

3John Maynard Keynes put forward the same distinction independently in his 1921 Treatise on

Probability(he was apparently not aware of Knights work, see Vercelli 1991: 73, fn 1) though

fleshed out in different technical ways (Vercelli 1991: 71-3). Keynes is largely credited with

bringing the idea of uncertainty into macro economics. In his later 1936General Theory of

Employment, Interest, and Money, Keynes thus famously noted that probabilities did not apply to

the realm of the truly uncertain.


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uncertainty The sense in which I am using the term is that in which the prospect of aEuropean war is uncertain, or the price of copper and the rate of interest twenty years

hence, or the obsolescence of a new invention About these matters there is noscientific basis on which to form any calculable probability whatever. We simply do not

know" (Keynes 1937: 213-4, my emphasis).4

Uncertainty accounts for the surprise of black swans (Taleb 2010), i.e, inherently

unpredictable and life-changing events such as 9/11, the 2008 financial crisis, or the widespread

adoption of the personal computer.5Even though with the benefit of hindsight some people may

look like they predicted them, a careful look at their predictive track record and a consideration

for the possibility of alternate unfoldings of histories will generally show that they simply got

lucky. Such events are genuinely unpredictable and appropriate preparation for these events is

4It used to be common to understand this seminal distinction on the basis of whether

probabilities are objective (risk) or subjective (uncertainty). This is no longer the case and the

distinction now has to do with an assumption of completenessness or incompleteness of

preference orderings over lotteries. To model Knightian uncertainy, Truman Bewley thus

suggested endowing an individual with multiple priors (i.e., multiple subjective probabilities)

and declaring actfto be preferred to actgif and only if for every prior, the expected utility off

exceeds that ofg(Bewley 1998). Bewley acknowledges that his approach does not capture

everything that Knight had in mind but to the extent it captures something it makes it less vague.

Nowadays, Bewley's approach is generally described as choice under ambiguity rather than

Knightian. Thank you to John Weymark for this point.

5Such unpredictable and high-stake events are called black swans in reference and homage to

Humes famous induction problem (just because you have only encountered white swans in the

past does not allow you to conclude that black swans do not exist).


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made all the harder if one cultivates the opposite delusion.6Uncertainty is also what explains

why pundits are so bad at making predictions about foreign-affairs (Tetlock 2005) and why

financial advisors are not worth the fees they are paid, at least not when it comes to making

predictions about stocks or interest rates (Taleb 2010, Gigerenzer 2014).7

We can thus define uncertainty as the absence of knowledge about not just the

probabilities attached to certain known outcomes, but the absence of knowledge about the very

nature and number of potential outcomes and their associated probabilities. Uncertainty is not the

known unknown, but in the unforgettable phrase of Donald Rumsfeld, it is the radically

unknown unknown. The presence of uncertainty in turn makes disagreements both more likely

to occur and harder to resolve: when we do not know which problems we will face in the future,

or what the consequences of major policy reforms are, it becomes easy to disagree about which

6Embracing the radical uncertainty of the world would often be a better preparation for such

events than vainly trying to calculate the risks ahead of time. To use an example from Nassim

Taleb, black swans are what happens on Thankgivings Eve to the turkey who grew too

confident over time that the person feeding him day in and day out for a year would never harm

him. Yes, in a predictable world of certainty, it is rational for the turkey to update his belief

along Bayesian principles, thus growing more confident that the risk of being killed decreases

over time. In an uncertain world, however, the turkey is bound to be surest of its safety on the

day it is going to get killed.

7Even if it turns out that the pundit was right or the investment manager made money on a

particular prediction, we are basically praising or paying them for being lucky. Like the turkey,

these experts make predictions for the coming year as if it were going to be like the last one,

which nothing guarantees.


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party is better equipped to govern the country, or which policy is the best solution to a particular

problem. And since uncertainty, by its very definition, implies that we cannot reliably place

probabilities on various outcomes, advocates of different positions can argue their cases on the

basis of different preferences, making the resolution of disagreements extremely difficult.

If uncertainty, besides complexity and the fact of disagreement, is an essential

circumstance of political life, as of most human affairs, what implications does this have for the

question of the best regime and who should rule? I will argue that it makes political equality

among the includedgiving all of them an equal right of speech and the principle of one man,

one voteecologically rational.

Democracy (political equality) as an ecologically rational heuristic

Many so-called proceduralist democrats take the principle of political equality as a starting point,

that is, as an axiom from which everything else derives, including our commitment to

deliberation and majority rule(e.g., Christiano 2008, Beitz 1989, Urbinati 2014, Schwartzberg

2013, Muirhead 2014). Here I follow a more instrumental strategy: political equality of the

included can be instrumentally justified by the fact that it is ecologically rational in the face of

radical uncertainty.

Democracy can be seen as a heuristic consisting of two components deliberation

among free and equal citizens, and majority rule. Both are undergirded by the same principle of

political equality, respectively embedded in the principle of isegoria (an equal right of speech)

and that of one person, one vote. In previous work (Landemore 2013), I have credited the

epistemic properties of both deliberation and majority rule to the inclusivenessof each procedure.

Building on the key insight of Lu Hong and Scott Page that cognitive diversity is a key


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component of group intelligence, in both the context of problem solving and prediction (Hong

and Page 2004; Page 2007), I argued that inclusiveness can serve as a proxy for cognitive

diversity. Specifically, when it comes to the first heuristic, inclusive deliberation, I conjectured a

Numbers Trump Ability Theorem by which it is more epistemically beneficial to have more

people enter the deliberation than restrict it to the smart few (Landemore 2013: 104). 8When it

comes to the second heuristic, majority rule, I relied on variants of the law of large numbers

(CJT, Miracle of Aggregation, and Hong and Pages Diversity Theorem) to justify why including

and counting more votes is likely to result in collective judgments more accurately tracking the

better of two options (Landemore 2013: Chapter Five).

As said earlier, however, I only considered inclusive procedures that happened to be

egalitarian: a democratic deliberation in which all are considered epistemic peers and given an

equal right to speak and a use of majority rule in which each individual is given one vote and one

vote only.9Yet one could imagine various forms of regime in which all are technically

8I there built on Hong and Pages Diversity Trumps Ability Theorem, simply pointing out that

having more people will more often than not increase the cognitive diversity of the deliberating

group, thus enhancing its problem-solving abilities and general epistemic performance.

9In my defense, I offer the outline of an argument in support of this position, albeit somewhat in

passing, in a passage where I commend random lotteries as the best selection mechanism for

representatives (Landemore 2013, Chapter 4; Landemore 2012). Random lotteries, I argue,

maximize cognitive diversity in the face of radical uncertainty about the political problems an

assembly would have to face over the course of its tenure by simply reproducing on the small

scale the composition of the larger group. In addressing the objection that we might be better off

oversamplingcognitive minorities, that is, giving more weightto some people over others, I


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enfranchised but not everyone has an equal right to speak in the public deliberation and in which

the votes of some count for more than the votes of others. Plural voting was famously defended

by John Stuart Mill, who recommended giving more votes to Ph.D. holders. It existed in New

Zealand until 1889, in, in Belgium until 1919, in England until 1948 and in Northern Ireland

until 1968. As to an unequal right of speech, it is unclear that it has ever been formalized as such

(in part because it is almost too easy to implement in practice). But even in representative

democracies where an equal right of speech is constitutionally ensured, one could argue that the

reality is still substantively unequal to the extent that the election system, the structure of media

ownership and media demand, which largely determine what gets printed and broadcasted, as

well as a host of other factors, skew the number and type of voices that we actually get to hear in

the public sphere. Whether we should call systems with plural right of speech and voting

democracies is an interesting question in and of itself but I will not touch on it here. The point

is that inclusive regimes premised on undemocratic deliberation (where aristocrats or experts or

make the argument that radical uncertainty makes this option unavailable and radical equality

preferable. When pressed to select a subgroup of decision-makers for feasibility reasons, we

should thus treat all candidates as equally likely to be epistemically knowledgeable or useful,

because we simply cannot predict the nature of the political problems the assembly of

representatives will have to face over the course of its tenure. Since we cannot know that, we

cannot oversample some people over others either and so we have to treat ex ante all individuals

as epistemic peers, even if,ex post, only a few will turn out to have known the answer or

contributed to the solution. Hence my defense of strict random selection, which treats all as

epistemic equals, is crucially based on radical uncertaintyas a feature of politics.


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some other caste have more voice) and plural voting are perfectly conceivable and perhaps even

seemingly appealing alternatives to a purely egalitarian decision-making regime that makes no

difference between the educated and the uneducated, the experts and the fools.

I now want to make the case for an egalitarian type of inclusiveness, that is, the political

equalityof the included, on epistemic grounds. While numbers may trump ability (under the

right conditions), the effectiveness of numbers at contributing to epistemic is maximized if each

number (each added individual voice or vote) is counted equally with all the others. Equality of

the included is indeed essential to the epistemic performance of both deliberation and majority

rule under certain states of the world.

The gist of the argument is this: if the bundle of political issues any human group faces

over time was complex but merely risky, that is in the realm of calculable probabilistic certainty,

then we would be better off weighing more heavily the voice and votes of expertsnamely

people with the time and smarts to perform the proper probability calculus. We could identify

these experts by looking at the past performance of a given population and figuring out those

with the best forecasting records over many years. Given more voice and votes to such

individuals would often mean giving almost zero weight, i.e., almost no voice and no vote at all,

to a vast majority of the population (I say almost because otherwise we are no longer even in the

real of inclusive regimes). A situation of risk would in fact justify oligarchy or expertocracy--

the limit cases of inclusive regimes where the voices and votes of most count for nothing.

The presence of uncertainty, however, defines an entirely new game. Uncertainty means

that we do not know and cannotknow what the future will be like, whether on the short term or

the long term, and whether, in particular, it will be like the past or radically different. As a result,

it is impossible to conclude anything from past trends in the forecasting or decision-making


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success of some individuals and it becomes rational, for reasons soon to be given, to follow a

simple heuristic: give everyone the same decision-power. This translates further into the two sub-

heuristics: equal right of speech, plus equal vote.

An analogy with finance might, surprisingly perhaps, help to understand the reasoning at

stake. A classic problem for investors is: How to build an optimal investment portfolio, that is,

one that maximizes returns while minimizing risks? Many people believe that this is a question

that only experts can answer and that the optimal portfolio must be built according to complex

equations involving arcane mathematics that only statistical wizards helped with super-

computers can perform. Not so, as it turns out.

Gerd Gigerenzer reports how Harry Markowitz himself, the winner of a Nobel Prize in

economics for his mean-variance investment model, followed a much simpler investment rule

in his personal life. Instead of optimizing Markowitz portfolios (named after him and

recommended by banks and investment managers the world over), Markowitz simply followed

the intuitive investment rule: one over N or invest your money equally. In other words, he

simply divided his savings equally between available investment options (Gigerenzer 2014).

How come the world expert on investment strategy does not follow his own recommendation? Is

this hypocrisy? No. It is simply the realization that the actual world is much more unpredictable

than the world modeled by economists. It is a world of radical uncertainty as opposed to one of

risk.

Empirical results confirm the general superiority of a simple rule of thumb like 1/N over

complex models. When tested empirically one over N does better than optimizing Markowitz

portfolios or a dozen other complex investment schemes (De Miguel et al. 2009). Note also that

the advantage of 1/N is that it does not need to use any past data and is fast and frugal that way.


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The complex models, by contrast, are useless without dataand little data is not much use either.

In the study mentioned above, the mean-variance (Markowitz) portfolio made use of a sizeable

10 years of stock data and still came in second to 1/N in 6 of the 7 tests. In all its blindness to

data, 1/N also outperformed a dozen other models when it came to making predictions about the

future value of a range of stocks. In fact, blindness to data is actually an advantage under

conditions of radical uncertainty because it saves the model from the risk of variance that affects

all models generalizing from data. For the Markowitz model to start paying off and doing better

than 1/N, one would have to be able to gather 500 years of data (De Miguel et al. 2009, cited in

Gigerenzer 2014: 93). And for such a model to keep being valuable over time, one would have to

assume that no more black swans were in store.

Gigerenzer derives from this example and a host of others a general logic about the

relative performance of simple rules of thumb versus complex models, not just in finance but in

any domain of life characterized by a degree of uncertainty . He lists three conditions for a simple

heuristic to be preferable to a complex equation:

1) The predictive uncertainty is large.

2) The number ofN(the range of options between which one must decide) is large.10

3) The learning sample (i.e., the available past data) is small.11

10The reason for why a largeNinvites going for a simpler rule is because the greater the N, the

more factors need to be estimated, and the more estimation errors risk being introduced in the

calculus. In contrast, simpler rules are less affected by more alternatives because it does not need

to make estimates from past data.

11If the learning sample is long (the data is large), it becomes possible to correct for mistakes

when using the complex model (the variance can be limited). If it is small, you do not have that


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Let us now apply the reasoning to politics. How large is uncertainty in politics? I have

argued in the second section of this essay that it is indeed large. In fact, the uncertainty of politics

is an important cause of the uncertainty of financial markets and in turn the uncertainty of

financial markets introduces a lot of uncertainty in politics. So a large degree of uncertainty in

politics seems a rather un-heroic assumption to make.

How large is the relevantNin politics? Here we are not talking about investment options

but numbers of possible decision-makers. An objector might wonder why we are not instead

considering laws or policy options but there is a simple answer to that: unlike investment options,

which are pre-defined in the financial market, laws and policies need to be formed and identified,

which can only happen through a public deliberation setting the agenda for further voting. So the

question is not how to choose between pre-defined laws and policies, but who to include and on

what terms in the decision-process that is meant to identify these options and then settle on one

of them. In mass industrialized democracies,Nthe number of potential decision-makers-- is

very large indeed. It is even quite large if we restrict our focus to the representatives of the

people, which usually number somewhere in the hundreds in existing Parliaments (and that is

only considering the legislative power, arguably the more central to the sovereignty of a

people).

Finally, how big is the learning sample in politics? That is, how many years of data about

the judgment of past decision-makers do we have to feed our models? Here I am tempted to

answer: little to none. There is to my knowledge no such thing as uncontroversial data about the

luxury and you are simply going to make too many mistakes relative to the length of time you

can experiment with the complex model.


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quality of political decisions (whether democratic, oligarchic, or dictatorial) over any amount of

time, in part because we did not have the conceptual tools (statistics among others) to attempt

such a thing and because of a profound reluctance since the second half of the 20 thcentury to

consider politics as a realm for right or wrong judgments. Philip Tetlocks ambitious Good

Judgment Project, which aims to compare the forecasts on foreign affairs of CIA agents with

those of ordinary citizens only armed with an internet connection is starting to dent the

nothingness of political data on that front but there is a daunting amount of ground to cover.

In any case, even when we accumulate enough big data about the past knowers among

ourselves (that is, figure out the people who turned out to be right about politics), this will

presumably be for domain-specific issues such a foreign-policy or economics, not for the

universal category of politics. So if the goal is to staff a generalist Congress as opposed to a

multitude of issue-specific assemblies, it is clear that we will never be able to identify universal

knowers as no one can have the amount and diversity of knowledge required to address all

possible political issues.

Further, even if we managed to accumulate extensive knowledge on a wide range of

issues, the causal structure of the world may well change faster than we can accumulate the

relevant data (this is the problem of what statisticians call non-stationarity), such that the data

will always only help predict the world of yesterday as opposed to the world of tomorrow.

The promise of big data in politics, which would allow us to identify a smarter decision-rule

giving more weight to the more knowledgeable than the simple heuristic democracy, is thus

bound to remain unfulfilled.


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Let me make explicit a key assumption of the reasoning thus far.12I have assumed

throughout that the larger populations knowledge is more evenly distributed across all political

issues than political experts knowledge. In other words I assume that whereas the knowledge of

the larger population tends to spread more or less evenly across all political topics, political

experts s knowledge will cluster on certain areas of politics. This point is important because if

experts were just as evenly distributed across issues as the larger population, then one would

have reasons to want to go against equally weighting the views of everyone and give more

weight to the experts.

This assumption of uneven distribution of experts knowledge across political issues

seems to me defensible given that most political problems have a diversity of geography, class,

race, and gender components, whereas expertness--at least as sociologically traceable --tends

to skew urban, wealthy, white, and male. It is also plausible given that only certain political

issues are given salience in the curriculum and training dispensed by universities and law,

business, and journalism schools where experts are typically formed. It is plausible, finally,

given that this distributive unevenness can be observed outside of politics, for example in the

contrast between Wikipedia and expert-written encyclopedia. Written by amateurs, Wikipedia

covers the whole range of human interests (including topics such as Paris Hilton and Game of

Thrones). By contrast, the topics covered by expert-written encyclopedias are comparatively

much fewer and narrower (more high-brow). Combined with the uncertainty of politics, this

difference in knowledge distribution makes it sensible to resist giving the experts, who know

more but on too little, more weight than the rest of the citizens, who know less but on a much

greater variety of topics.

12Thank you to Scott Page for suggesting this clarification.


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An objector might want to insist: but if we observe, empirically, that some people

outperform others in terms of political predictions, why dont we give those people just a bit

more weight? Shouldnt we get more confident over time that such super-predictors will know

next time and thus increase the weight of their voice and votes? Yes, but only if we knew for

certain that the world has not changed. But we do not know that and never will.13

The objector might press the point by distinguishing between short-term and long-term

horizons. Sure, in the long run everything is too uncertain and unpredictable so maybe

democracy makes sense on that horizon, as does 1/N in finance for someone who does want to

bother managing their stocks on a daily basis. But at a sufficiently low level of resolutiona

couple of months, perhaps a couple of years in politicssurely things are a bit more stable and

predictable, both when it comes to the nature of the problems the group is going to face and the

epistemic competence of the voters or potential decision-makers. The market example, after all,

may invite the objection that there is a lot of correlation between the value of a stock today and

its value the day after. So while in the long run we might want to keep everyone around, in the

short term there might be very good instrumental reason to give some actors more epistemic

weight than others. If the demos, for example, is debating how much capitalization banks must

have on hand why oughtn't one believe that Senator Elizabeth Warren will be better at the task

than a college freshman and weight their votes accordingly?14

13In the face of our ignorance, this reasoning, which would support plural voting for example,

amounts to making the mistake of the turkey. It might not hurt you for a while but come

Thanksgivings Eve, you wont even live to regret it.

14I owe this challenging point to William Berger.


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This objection is particularly interesting in that it has the structure of the famous paradox

of the Sorites. The same way that removing a single grain does not turn a heap into a non-heap

and yet at some point we will have moved from a heap to a non-heap, the objector argues that we

can move from a situation in which the world is too unpredictable (a situation of uncertainty) to

one where it is at least probabilistically predictable (a situation of risk), even though shortening

the long-term horizon by one day at a time should not make it less predictable at any point. So

while the objector acknowledges that on the long term it does not make sense to bet the house on

Apple stocks only or the Elizabeth Warren of this country only, he would still like to move us

toward undemocratic deliberation and a plural voting scheme on the short term.

As in the paradox of the Sorites, however, an important problem is that the boundary

between a heap and a non-heap, or here the predictable future and the unpredictable one (and

thus the domain of an inegalitarian decision-rule and that of an egalitarian one), if it exists, is

bound to be unknowable. Where should we draw the line between short term and long term? At

what point do we take away the decision power from Elizabeth Warren and redistribute it more

equitably? This idea that there is such a thing as a predictable short-term horizon in which the

nature and structure of problems is going to stay the same is a result of hindsight bias. Only in

retrospect are we able to identify historically stable short-term periods, like say, a business

cycle or a peace truce. Ex ante, however, it is impossible to tell whether Apple stocks will keep

rising or if we are on the eve of another 9/11 or Great Recession. In politics as in economics, the

timing is everything (and getting it wrong can be extremely costly).

That said, the point of giving more weight to Elizabeth Warrens vote in a decision about

bank capitalization is well taken. But it applies only once we (as a group) are already in a

position to identify the nature of the problems and who is best situated to answer them in the


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group because these problems are now immediately on the table in front of us. At that point we

are no longer trying to make a prediction about whose voice and votes will matter more in the

resolution of future problems, because the problems have now gone from possibility to

reality and we can now proceed to make decisions about them through the various means at our

disposal, which include experts. In other words, delegating to experts or a subset of people who

have plausible credentials with respect to the task at hand can only be done as a secondary step,

not a first one, once we have moved from a situation of uncertainty to one of risk.

Notice that even in a context of risk, solving problems democratically may still present

advantages (whenever many minds are better than one) and the trade-off between more or less

inclusive as well as more or less egalitarian decision rules will then depend on various concerns,

including the cost of mistakes associated with each decision-rule. Should it be the case, for

example, that democracy reliably avoids the most egregious mistakes (more so than oligarchy or

expertocracy), it might be the superior decision rule in the context of risk even if it leads to a

slightly higher number of (less costly) mistakes being made.15

But in any case, the switch from

considering all the included individuals as epistemic peers to weighing them differently cannot

be justified as a matter of short-term versus long-term. It is, rather, a matter of moving from a

situation where we are trying to predict the nature of political problems to come (which is

impossible) and a situation where the problems have already materialized and we are trying to

understand and solve them (which is perhaps all the objector means by short-term). In the first

15 As Taleb nicely makes the point with his emphasis on the distinction between probability and

expectation (Taleb 2005: 99), it does not matter how often a procedure or investment strategy is

wrong, what matters is the average expected value of that decision-rule.


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case scenario, if it turns out that the next bubble to burst is related to students debt, say, it might

be very valuable to have retained some college freshmen in the decision pool.

We now should be able to see why democracy proves to have an ecological advantage

over not just oligarchy, but inclusive decision-rules that do not give everyone the same weight,

such as democracies with plural voting schemes. The parallel with investment strategies suggests

that in the same way that an investor should diversify her portfolio following the simple heuristic

One over N rather than a mean-variance strategy, a group should diversify the risk of

collective decision-making by following the simple heuristic democracy or equal right of

speech followed by one man, one vote rather than oligarchy or even plural voting schemes.

Radical uncertainty makes radical equality the only rational heuristic.

Going back to the metaphor of the maze, that means that at least one good reason (there

certainly are others) to include everyone on an equal footing is that at every new fork in the maze

we cannot built anything reliable on the accumulated knowledge of who turned out to be right in

the previous round. We just dont know enough about the structure of the maze and its problems.

It is thus better to include everyone, and everyone equally.

Conclusion:

Whereas inclusiveness of all is rational as a proxy for cognitive diversity in the face of complex

problems requiring widely distributed knowledge and multiple talents and perspectives, political

equality among the included is rational specifically for the reason that radical uncertainty is an

ineradicable feature of politics. We just do not know, ex ante, who among the included will turn

out, ex post, to have the kind of knowledge or bring the kind of perspective needed to solve the

problem because we simply do not know enough about the world to be able to predict what


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problems we are going to face as a group in the future, no matter how much knowledge of the

past we may have. So not only is there no epistemic reason to exclude anyone ex ante, but there

is no epistemic reason either to give anyones voice or vote more weight in the deliberative and

aggregative phase of the decision-making process. As a result, the very nature of politics

complexity and uncertainty--has not only inclusive but radically egalitarian consequences: it

demands not just including every voice and vote, but counting them equally as well.

The assumption of radical uncertainty as a key feature of politics is not particularly

controversial or heroic. Democracy is thus useful not just where problems are complex and

knowledge is widely distributed. It is more importantly useful where we dont even know what

the distribution of knowledge is like because we have no clue what the structure of future

problems is going to be and therefore the kind of solutions and knowledge they will require. In

other words, given the radical uncertainty of politics, we can no more attribute different weights

to certain people than we can exclude others altogether. It is only in the process of figuring

things out as a group and defining the structure of the problem that we can then move on to an

epistemic division of labor that entitles some (those we call experts) to weigh in more heavily on

the decision. But as a first stab at dealing with the unknown collectively, democracy remains the

simplest, fastest, and smartest collective rule. The principle of political equality that underlies

democracy can thus be cast an as an ecologically rational collective heuristic for politics, in the

same way that Divide your money equally among investment options is an ecologically

rational individual heuristic for investment.


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