How is (internal) irrationality possible?

Much unhappiness (...) has less to do with not getting what we want, and more to do with not wanting what we like.(Gilbert & Wilson, 2000)

Yes, we should make choices by multiplying probabilities and utilities, but how can we possibly do this if we can’t estimate those utilities beforehand? (Gilbert, 2006)

When we preview the future and prefeel its consequences, we are soliciting advice from our ancestors. This method is ingenious but imperfect. (Gilbert, et al. 2007)

Although we easily and intuitively assess each other’s behavior, speech, or character as irrational, providing a non-trivial account of irrationality might be tricky (something we philosophers like to deal with!) Let’s distinguish internal and external assessment of rationality: an internal (or subjective) assessment of rationality is an evaluation of the coherence of intentions, actions and plans. An external (or objective) assessment of rationality is an evaluation of the effectiveness of a rule or procedure. It assesses the optimality of a rule for achieving a certain goal. An action can be rational from the first perspective but not from the second one, and vice versa. Hence subjects’ poor performance in probabilistic reasoning can be internally rational without being externally rational: the Gambler’s fallacy is and will always be a fallacy: it is possible, however, that fallacious reasoners follow rational rules, maximizing an unorthodox utility function. Consequently, it is easy to understand how one can be externally irrational, but less easy to make sense of internal irrationality.

An interesting suggestion comes from hedonic psychology, and mostly Dan Gilbert’s research: irrationality is possible if agents fail to want things they like. Gilbert research focuses on Affective Forecasting, i.e., the forecasting of one's affect (emotional state) in the future (Gilbert, 2006; Wilson & Gilbert, 2003): anticipating the affective valence, intensity, duration and nature of specific emotions. Just like Tversky and Kahneman studied biases in probabilistic reasoning, he and his collaborator study biases in hedonistic reasoning.

In many cases, for instance, people do not like or dislike an event as much as they thought they would. They want things that do not promote welfare, and not want things that would promote their welfare. This what Gilbert call “miswanting”. We miswant, explain Gilbert, because of affective forecasting biases.

Take for instance impact biases: subject overestimate the length (durability bias) or intensity (intensity bias) of future emotive states (Gilbert et al., 1998):

“Research suggests that people routinely overestimate the emotional impact of negative events ranging from professional failures and romantic breakups to electoral losses, sports defeats, and medical setbacks”. (Gilbert et al., 2004).

They also underestimate the emotional impact of positive events such as winning a lottery (Brickman et al., 1978): newly rich lottery winners rated their happiness at this stage of their life as only 4.0, (on a 6-point scale, 0 to 5) which does not differ significantly from the rating of the control subjects. Also surprising to many people is the fact that paraplegics and quadriplegics rated their lives at 3.0, which is above the midpoint of the scale (2.5). In another study, Boyd et al., (1990) solicited the utility of life with a colostomy from several different groups: patients who had rectal cancer and who had been treated by radiation, patients who had rectal cancer and who had been treated by a colostomy, physicians who had experience treating patients with gastrointestinal malignancies, and two groups of healthy individuals. The patients with a colostomy and the physicians rated life with a colostomy significantly higher than did the other three groups. Another bias is the Empathy gap: humans fail to empathize or predict correctly how they will feel in the future, i.e. what kind of emotional state they will be in. Sometimes, we fail to take into account how much our psychological “immune system” will ameliorate reactions to negative events. People do not realize how they will rationalize negative outcomes once they occur (the Immune neglect). People also often mispredict regret (Gilbert et al., 2004b):

the top six biggest regrets in life center on (in descending order) education, career, romance, parenting, the self, and leisure. (…) people's biggest regrets are a reflection of where in life they see their largest opportunities; that is, where they see tangible prospects for change, growth, and renewal. (Roese & Summerville, 2005).

So a perfectly rational agent, at time t, would choose to do X at t+1 given what she expects her future valuation of X to be. As studies showed, however, we are bad predictors of our own future subjective appreciation. The person we are at t+1 may not totally agree with the person we were at t. So, in one sense, this gives a non-trivial meaning to internal irrationality: since our affective forecasting competence is biased, we may not always choose what we like or like what we choose. Hedonic psychology might have identified incoherence between intentions, actions and plans, an internal failure in our practical rationality.

Recommended reading:

• Gilbert, D. T., & Wilson, T. D. (2007). Prospection: Experiencing the Future. Science, 317(5843), 1351-1354.

References

• Berns, G. S., Chappelow, J., Cekic, M., Zink, C. F., Pagnoni, G., & Martin-Skurski, M. E. (2006). Neurobiological Substrates of Dread. Science, 312(5774), 754-758.
• Boyd, N. F., Sutherland, H. J., Heasman, K. Z., Tritchler, D. L., & Cummings, B. J. (1990). Whose Utilities for Decision Analysis? Med Decis Making, 10(1), 58-67.
• Brickman, P., Coates, D., & Janoff-Bulman, R. (1978). Lottery Winners and Accident Victims: Is Happiness Relative? J Pers Soc Psychol, 36(8), 917-927.
• Gilbert, D. T. (2006). Stumbling on Happiness (1st ed.). New York: A.A. Knopf.
• Gilbert, D. T., & Ebert, J. E. J. (2002). Decisions and Revisions: The Affective Forecasting of Changeable Outcomes. Journal of Personality and Social Psychology, 82(4), 503–514.
• Gilbert, D. T., Lieberman, M. D., Morewedge, C. K., & Wilson, T. D. (2004a). The Peculiar Longevity of Things Not So Bad. Psychological Science, 15(1), 14-19.
• Gilbert, D. T., Morewedge, C. K., Risen, J. L., & Wilson, T. D. (2004b). Looking Forward to Looking Backward. The Misprediction of Regret. Psychological Science, 15(5), 346-350.
• Gilbert, D. T., Pinel, E. C., Wilson, T. D., Blumberg, S. J., & Wheatley, T. P. (1998). Immune Neglect: A Source of Durability Bias in Affective Forecasting. J Pers Soc Psychol, 75(3), 617-638.
• Gilbert, D. T., & Wilson, T. D. (2000). Miswanting: Some Problems in the Forecasting of Future Affective States. Feeling and thinking: The role of affect in social cognition, 178–197.
• Kermer, D. A., Driver-Linn, E., Wilson, T. D., & Gilbert, D. T. (2006). Loss Aversion Is an Affective Forecasting Error. Psychological Science, 17(8), 649-653.
• Loomes, G., & Sugden, R. (1982). Regret Theory: An Alternative Theory of Rational Choice under Uncertainty. The Economic Journal, 92(368), 805-824.
• Roese, N. J., & Summerville, A. (2005). What We Regret Most... And Why. Personality and Social Psychology Bulletin, 31(9), 1273.
• Seidl, C. (2002). Preference Reversal. Journal of Economic Surveys, 16(5), 621-655.
• Slovic, P., Finucane, M., Peters, E., & MacGregor, D. G. (2002). Rational Actors or Rational Fools: Implications of the Affect Heuristic for Behavioral Economics. Journal of Socio-Economics, 31(4), 329-342.
• Srivastava, A., Locke, E. A., & Bartol, K. M. (2001). Money and Subjective Well-Being: It's Not the Money, It's the Motives. J Pers Soc Psychol, 80(6), 959-971.
• Thaler, A., & Tversky, R. H. (1990). Anomalies: Preference Reversals. Journal of Economic Perspectives, 4, 201-211.
• Wilson, T. D., & Gilbert, D. T. (2003). Affective Forecasting. Advances in experimental social psychology, 35, 345-411.

My brain has a politics of its own: neuropolitic musing on values and signal detection

Political psychology (just as politicians and voters) identifies two species of political values: left/right, or liberalism/conservatism. Reviewing many studies, Thornhill & Fincher (2007) summarizes the cognitive style of both ideologies:

Liberals tend to be: against, skeptical of, or cynical about familiar and traditional ideology; open to new experiences; individualistic and uncompromising, pursuing a place in the world on personal terms; private; disobedient, even rebellious rulebreakers; sensation seekers and pleasure seekers, including in the frequency and diversity of sexual experiences; socially and economically egalitarian; and risk prone; furthermore, they value diversity, imagination, intellectualism, logic, and scientific progress. Conservatives exhibit the reverse in all these domains. Moreover, the felt need for order, structure, closure, family and national security, salvation, sexual restraint, and self-control, in general, as well as the effort devoted to avoidance of change, novelty, unpredictability, ambiguity, and complexity, is a well-established characteristic of conservatives. (Thornhill & Fincher, 2007).

In their paper, Thornhill & Fincher presents an evolutionary hypothesis for explaining the liberalism/conservatism ideologies: both originate from innate adaptation to attachement, parametrized by early childhood experiences. In another but related domain Lakoff (2002) argued that liberals and conservatives differs in their methaphors: both view the nation or the State as a child, but they hold different perspectives on how to raise her: the Strict Father model (conservatives) or the Nurturant Parent model (liberals); see an extensive description here). The first one

posits a traditional nuclear family, with the father having primary responsibility for supporting and protecting the family as well as the authority to set overall policy, to set strict rules for the behavior of children, and to enforce the rules [where] [s]elf-discipline, self-reliance, and respect for legitimate authority are the crucial things that children must learn.

while in the second:

Love, empathy, and nurturance are primary, and children become responsible, self-disciplined and self-reliant through being cared for, respected, and caring for others, both in their family and in their community.

In the October issue of Nature Neuroscience, a new research paper by Amodio et al. study the "neurocognitive correlates of liberalism and conservatism". The study is more modest than the title suggests. Subject were submitted to the same test, a Go/No Go task (click when you see a "W" don't click when it's a "M"). The experimenters then trained the subjects to be used to the Go stimuli; on a few occasions, they were presented with the No Go stimuli. Since they got used to the Go stimuli, the presentation of a No Go creates a cognitive conflict: balancing the fast/automatic/ vs. the slow/deliberative processing. You have to inhibit an habit in order to focus on the goal when the habit goes in the wrong direction. The idea was to study the correlation between political values and conflict monitoring. The latter is partly mediated by the anterior cingulate cortex, a brain area widely studied in neuroeconomics and decision neuroscience (see this post). EEG recording indicated that liberals' neural response to conflict were stronger when response inhibition was required. Hence liberalism is associated to a greater sensibility to response conflict, while conservatism is associated with a greater persistence in the habitual pattern. These results, say the authors, are

consistent with the view that political orientation, in part, reflects individual differences in the functioning of a general mechanism related to cognitive control and self-regulation

Thus valuing tradition vs. novelty, security vs. novelty might have sensorimotor counterpart, or symptoms. Of course, it does not mean that the neural basis of conservatism is identified, or the "liberal area", etc, but this study suggest how micro-tasks may help to elucidate, as the authors say in the closing sentence, "how abstract, seemingly ineffable constructs, such as ideology, are reflected in the human brain."

What this study--together with other data on conservatives and liberal--might justify is the following hypothesis: what if conservatives and liberals are natural kinds? That is, "homeostatic property clusters", (see Boyd 1991, 1999), categories of "things" formed by nature (like water, mammals, etc.), not by definition? (like supralunar objects, non-cat, grue emerald, etc.) Things that share surface properties (political beliefs and behavior) whose co-occurence can be explained by underlying mechanims (neural processing of conflict monitoring)? Maybe our evolution, as social animals, required the interplay of tradition-oriented and novelty-oriented individuals, risk-prone and risk-averse agents. But why, in the first place, evolution did not select one type over another? Here is another completely armchair hypothesis: in order to distribute, in the social body, the signal detection problem.

What kind of errors would you rather do: a false positive (you identify a signal but it's only noise) or a false negative (you think it's noise but it's a signal)? A miss or a false alarm? That is the kind of problems modeled by signal detection theory (SDT): since there is always some noise and you try to detect signal, you cannot know in advance, under radical uncertainty, what kind of policy you should stick to (risk-averse or risk-prone. "Signal" and "noise" are generic information-theoretic terms that may be related to any situation where an agent tries to find if a stimuli is present:

Is is rather ironic that signal detection theorists employ the term liberal* and conservative* (the "*" means that I am talking of SDT, not politics) to refer to different biases or criterions in signal detection. A liberal* bias is more likely to set off a positive response ( increasing the probability of false positive), whereas a conservative* bias is more likely to set off a negative response (increasing the probability of false negative). The big problem in life is that in certain domains conservatism* pay, while in others it's liberalism* who does (see Proust 2006): when identifying danger, a false negative is more expensive (better safe than sorry) whereas in looking for food a false positive can be more expensive better (better satiated than exhausted). So a robust criterion is not adaptive; but how to adjust the criterion properly? If you are an individual agent, you must altern between liberal* and conservative* criterion based on your knowledge. But if you are part of a group, liberal* and conservative* biases may be distributed: certains individuals might be more liberals* (let's send them to stand and keep watch) and other more conservatives* (let's send them foraging). Collectively, it could be a good solution (if it is enforced by norms of cooperation) to perpetual uncertainty and danger. So if our species evolved with a distribution of signal detection criterions, then we should have evolved different cognitive styles and personality traits that deal differently with uncertainty: those who favor habits, traditions, security, and the others. If liberal* and conservative* criterions are applied to other domains such as family (an institution that existed before the State), you may end up with the Strict Father model and the Nurturant Parent model; when these models are applied to political decision-making, you may end up with liberals/conservatives (no "*"). That would give a new meaning to the idea that we are, by nature, political animals.


Related posts

• A Neuropolitic Look at Political Psychology
• The Political Brain
• A Neuroecomic Picture of Valuation
  

Links

• A clear discussion of the study at The Cognitive Daily.
• Political Psychology (Blackwell Publishing Journal)
• See their special issue: Political Psychology and Social Neuroscience: Strange Bedfellows or Comrades in Arms?, especially this review paper.
• Neuropolitics.org (Ezine)
• Neuropolitics mailing list.
• A presentation of Darren Schreiber research (not professor at UCSD in the Political Science Department) on neural studies of differences between political novices and sophisticates : "When asked political questions, political novices show higher activation in the prefrontal lobes, where deliberative thinking takes place. Political sophisticates show higher levels of temporal lobe activity, which suggests they 'have more meaning attached to these questions' "

References

• Amodio, D. M., Jost, J. T., Master, S. L., & Yee, C. M. (2007). Neurocognitive correlates of liberalism and conservatism. Nature Neuroscience, 10(10), 1246-1247
• Boyd, R. (1991). Realism, Anti-Foundationalism and the Enthusiasm for Natural Kinds. Philosophical Studies, 61(1 - 2), 127.
• Boyd, R. (1999). Kinds, Complexity and Multiple Realization. Philosophical Studies, 95(1 - 2), 67.
• Lakoff, G. (2002). Moral Politics: How Liberals and Conservatives Think. Chicago: University of Chicago Press (read chapter 2 here)
• Proust, J. (2006 ). Metacognition and Animal Rationality. In S. Hurley & M. Nudds (Eds.), Rational Animals. Oxford: Oxford University Press.
  
• Thornhill, R., & Fincher, C. L. (2007). What is the relevance of attachment and life history to political values? Evolution and Human Behavior, 28(4), 215-222.
• Thorisdottir, H., Jost, J. T., Liviatan, I., & Shrout, P. E. (2007). Psychological Needs and Values Underlying Left-Right Political Orientation: Cross-National Evidence from Eastern and Western Europe. Public Opin Q, nfm008.

Natural Rationality for Newbies

Decision-making, as I routinely argue in this blog, must be understood as entrenched in a richer theoretical framework: Darwin’s economy-of-nature. According to this principle, animals could be modeled as economic agents and their control systems could be modeled as economic devices. All living beings are thus deciders, strategists or traders in the economy of reproduction and survival.

When he suggested that nature is an economy, Darwin paved the way for a stronger interaction between biology and economics. One of the consequences of a bio-economic approach is that decision-making becomes an increasingly important topic. The usual, commonsense construal of decision-making suggests that it is inherently tied to human characteristics, language in particular. If that is the case, then talk of animal decisions is merely metaphorical. However, behavioral ecology showed that animals and human behavior is constrained by economic parameters and coherent with the economy-of-nature principle. Neuroeconomics suggest that the neural processing follow the same logic. Dopaminergic systems drive animals to achieve certain goals while affective mechanisms place goals and action in value spaces. These systems, although they were extensively studied in humans, are not peculiar to them: humans display a unique complexity of goals and values, but this complexity relies partly on neural systems shared with many other animals: the nucleus accumbens and the amygdala, for instance are common in mammals. Brainy animals evolved an economic decision-making organ that allows them to cope with complex situations. As Gintis remarks, the complexity and the metabolic cost of central nervous systems co-evolved in vertebrates, which suggests that despite their cost, brains are designed to make adaptive decision[i].

Hence decision-making should be analyzed similarly as, and occupies an intellectual niche analogous to, the concept of cooperation. Nowadays, the evolutionary foundations, neural substrates, psychological mechanisms, formal modeling and philosophical analyses of cooperation constitutes a coherent—although not unified—field of inquiry [ii]. The nature of prosocial behavior, from kin selection to animal cooperation to human morality, is best understood by adopting a naturalistic stances that highlights both the continuity of the phenomenon and the human specificity. Biological decision-making deserves the same eclecticism.

Talking about biological decision-making comes at a certain conceptual price. As many philosophers pointed out, whenever one is describing actions and decisions, one is also presupposing the rationality of the agent[iii]. When we say that agent A chose X, we suppose that A had reasons, preferences, and so on. The default assumption is that preferences and actions are coherent: the firsts caused the seconds, and the seconds are justified by the firsts. The rationality philosophers are referring to, however, is a complex cognitive faculty, that requires language and propositional attitudes such as beliefs and desires. When animals forage their environment, select preys, patches, or mates, no one presupposes that they entertain beliefs or desires. There is nonetheless a presupposition that “much of the structure of the internal mental operations that inform decisions can be viewed as the product of evolution and natural selection”.[iv] Thus, to a certain degree, the neuronal processes concerned with the use of information are effective and efficient, otherwise natural selection would have discarded them. I shall label these presuppositions, and the mechanisms it might reveal, “natural rationality”. Natural rationality is a possibility condition for the concept of biological decision-making and the economy-of-nature principle. One needs to presuppose that there is a natural excellence in the biosphere before studying decisions and constraints.

More than a logical prerequisite, natural rationality concerns the descriptive and normative properties of the mechanisms by which humans and other animals make decisions. Most concepts of rationality take only the descriptive or the normative side, and hence tend to describe cognitive/neuronal processes without concern for their optimality, or state ideal conditions for rational behavior. For instance, while classical economics considers rational-choice theory either as a normative theory or a useful fiction, proponents of bounded rationality or ecological rationality refuse to characterize decision-making as optimization.[v] Others advocate a strong division of labor between normative and descriptive project: Tversky and Kahneman, for instance, concluded from their studies of human bounded rationality that the normative and descriptive accounts of decision-making are two separate projects that “cannot be reconciled”[vi]

The perspective I suggest here is that we should expect an overlap between normative and descriptive theories, and that the existence of this overlap is warranted by natural selection. On the normative side, we should ask what procedures and mechanisms biological agents should follow in order to make effective and efficient decision given all their constraints in the economy of nature. On the descriptive side, we must assess whether a procedure succeeds in achieving goals or, conversely, what goals could a procedure aim at achieving. If there is no overlap between norms and facts, then either norms should be reconceptualized or facts should be scrutinized: it might be the case that norms are unrealistic or that we did not identify the right goal or value.

This accounts contrasts with philosophers (e.g. Dennett or Davidson) who construe rationality as an idealization and researchers who preach the elimination of this concept because of its idealized status (evolutionary psychologists, for instance[vii]). Thus, rationality can be conceived not as an a priori postulate in economy and philosophy, but as an empirical and multidisciplinary research program. Quine once said that “creatures inveterately wrong in their inductions have a pathetic but praiseworthy tendency to die out before reproducing their kind”[viii]. Whether it is true for inductions is still open to debate, but I suggest that it clearly applies to decisions.

Related posts

• Decision-making and the economy of nature
• The Economy of Nature: A Brief Introduction
• Rational performance and behavioral ecology

Notes and references

• [i] (Gintis, 2007, p. 3)
• [ii] See for instance how neuroscience, game theory, economic, philosophy, psychology and evolutionary theory interact in (E. Fehr & Fischbacher, 2002; Ernst Fehr & Fischbacher, 2003; Hauser, 2006; Penner et al., 2005).
• [iii] (Davidson, 1980; Dennett, 1987; Popper, 1994).
• [iv] (Real, 1994, p. 4)
• [v] (Chase et al., 1998; Gigerenzer, 2004; Selten, 2001)
• [vi] (Tversky & Kahneman, 1986, p. s272)
• [vii][vii] (Cosmides & Tooby, 1994)
• [viii] (Quine, 1969, p. 126)
  

References

• Chase, V. M., Hertwig, R., & Gigerenzer, G. (1998). Visions of Rationality. Trends in Cognitive Science, 2(6), 206-214.
• Cosmides, L., & Tooby, J. (1994). Better Than Rational: Evolutionary Psychology and the Invisible Hand. The American Economic Review, 84(2), 327-332.
• Davidson, D. (1980). Essays on Actions and Events. Oxford: Oxford University Press.
• Dennett, D. C. (1987). The Intentional Stance. Cambridge, Mass.: MIT Press.
• Fehr, E., & Fischbacher, U. (2002). Why Social Preferences Matter: The Impact of Non-Selfish Motives on Competition, Cooperation and Incentives. Economic Journal, 112, C1-C33.
• Fehr, E., & Fischbacher, U. (2003). The Nature of Human Altruism. Nature, 425(6960), 785-791.
• Gigerenzer, G. (2004). Fast and Frugal Heuristics: The Tools of Bounded Rationality. In D. Koehler & N. Harvey (Eds.), Blackwell Handbook of Judgment and Decision Making (pp. 62–88). Oxford: Blackwell.
• Gintis, H. (2007). A Framework for the Unification of the Behavioral Sciences. Behavioral and Brain Sciences, 30(01), 1-16.
• Hauser, M. D. (2006). Moral Minds : How Nature Designed Our Universal Sense of Right and Wrong. New York: Ecco.
• Penner, L. A., Dovidio, J. F., Piliavin, J. A., & Schroeder, D. A. (2005). Prosocial Behavior: Multilevel Perspectives. Annual Review of Psychology, 56(1), 365-392.
• Popper, K. R. (1994). Models, Instruments, and Truth: The Status of the Rationality Principle in the Social Sciences. In The Myth of the Framework. In Defence of Science and Rationality
• Quine, W. V. O. (1969). Ontological Relativity, and Other Essays. New York,: Columbia University Press.
• Real, L. A. (1994). Behavioral Mechanisms in Evolutionary Ecology: University of Chicago Press.
• Selten, R. (2001). What Is Bounded Rationality ? . In G. Gigerenzer & R. Selten (Eds.), Bounded Rationality: The Adaptive Toolbox (pp. 13-36). MIT Press: Cambridge, MA.
• Tversky, A., & Kahneman, D. (1986). Rational Choice and the Framing of Decisions. The Journal of Business, 59(4), S251-S278.

Neuroeconomics in the Annual Review of Psychology

A great team of (neuro) economists/psychologists, George Loewenstein, Scott Rick and Jonathan Cohen, wrote an extensive review paper about neuroeconomics for the 2008 Annual Review of Psychology. It presents all important research papers, discusses how neuroeconomics shed light--from a psychological and economic point of view--on decision-making under risk and uncertainty, intertemporal choice, and social decision making and, finally, show how this research can contribute to psychology. It is a highly recommended paper. One important missing topic, however, is all the neuroeconomics literature about hormomes and behavior, such as Paul Zak's lab research, for instance, the study that shows that oxytocin increases trust (and generosity; see this post): players transfer more money, in the trust game, after inhaling oxytocin (an hormone involved in social cognition, fear reduction, bonding, love, etc.). Anyway, here is a short summary (from the paper):

Neuroeconomics has further bridged the once disparate fields of economics and psychology, largely due to movement within economics. Change has occurred within economics because the most important findings in neuroeconomics have posed a challenge to the standard economic perspective.

Neuroeconomics has primarily challenged the standard economic assumption that decision making is a unitary process—a simple matter of integrated and coherent utility maximization—suggesting instead that it is driven by the interaction between automatic and controlled processes.

Neuroeconomic research has focused most intensely on decision making under risk and uncertainty, but this line of research provides only mixed support for a dual systems perspective.

The extent to which intertemporal choice is generated by multiple systems with conflicting priorities is perhaps the most hotly debated issue within neuroeconomics. However, a majority of the evidence favors a multiple systems perspective.

Neuroeconomic research on social preferences is highly supportive of a dual systems account, although the most prominent studies come to conflicting conclusions regarding how selfinterest and fairness concerns interact to influence behavior.

Neuroeconomics may ultimately influence psychology indirectly, via its influence on economics (e.g., by inspiring economic models increasingly grounded in psychological reality), and directly, by addressing debates of interest within psychology (e.g., whether multiple systems operate sequentially or in parallel to influence behavior).

References

• Loewenstein, G., Rick, S., & Cohen, J. (2008). Neuroeconomics. Annual Review of Psychology, 59(1). (published online as a Review in Advance on September 17, 2007)

new website + contact info

please note that my homepage URL and academic contact info recently changed:

• my new website is : http://hardyvallee.net

• my address is now:

Benoit Hardy-Vallée
Department of Philosophy
University of Toronto
Jackman Humanities Building
170 St. George St., 4th Floor,
Toronto, ON, M5R 2M8
Phone: (416) 978-3316
Fax: (416) 978-8703
Email: ben.hardy.vallee@utoronto.ca

• You can download my vCard here

• I am also now on Facebook
  
  
• Natural Rationality stays at the same URL:

.blogspot.com

The Stuff of Thought

Unless you live on a desert island, you might have heard of Steven Pinker's new book, The Stuff of Thought. Language as a Window into Human Nature.

If you are interested in knowing more about it, here are an excerpt online, a book review and an interview.

Last but not least, two video lectures of Pinker at Ted Talks (an amazing collection of lecture by great scholars):

" In an exclusive preview of his new book, The Stuff of Thought, Steven Pinker looks at language, and the way it expresses the workings of our minds. By analyzing common sentences and words, he shows us how, in what we say and how we say it, we're communicating much more than we realize."





"In a preview of his next book, Steven Pinker takes on violence. We live in violent times, an era of heightened warfare, genocide and senseless crime. Or so we've come to believe. Pinker charts a history of violence from Biblical times through the present, and says modern society has a little less to feel guilty about."

Strong reciprocity, altruism and egoism

Proponents of the Strong Reciprocity Hypothesis (i.e., Bowles, Gintis, Boyd, Fehr, Heinrich, etc., I will call them “The Collective”) claim that human being are strong reciprocators: they are willing to sacrifice resources in order to reward fair and punish unfair behavior even if there is no direct or future reward. Thus we are, according to the Collective, innately endowed with pro-social preferences and aversion to inequity. Those who advocate strong reciprocity take it to be a a ‘genuine’ altruistic force, not explained by other motives. Strong reciprocity is here contrasted with weaker form of reciprocity, such as: cooperating with someone because of genetic relatedness (kinship), because one follows a tit-for-tat pattern (direct reciprocity), wants to establish a good reputation (indirect reciprocity) or displays signs of power or wealth (coslty signaling). Thus our species is made, ceteris paribus, of altruistic individuals that tend to cooperate with cooperators and punish defectors, even at a cost. Behavioral economics showed how people are willing to cooperate in games such as the prisoner’s dilemma, the ultimatum game or the trust game: they do not cheat in the first one, offer fair split in the second and transfer money in the third.

Could it be possible, however, that this so-called altruism is instrumental? I don’t think it is always, some cases require closer scrutiny. For instance, in the Ultimatum Game, there is a perfectly rational and egoist reason to make a fair offer, such as a 50-50 split: it is the best—from one’s point of view—solution to the trade-off between making a profit and proposing a split that the other player will accept: if you propose more, you loose more money: if you propose less, you risk a rejection. In non-market integrated culture where a 20-80 split is not seen as unfair, proposers routinely offer such splits, because they know it will be accepted.

It can also be instrumental in a more basic sense, for instance in participating to the propagation of our genes. For instance, (Madsen et al., 2007) showed that individuals behave more altruistically toward their own kin when there is a significant genuine cost (such as pain), an attitude also mirrored in study with questionnaires (Stewart-Williams, 2007): when the cost of helping augments, subjects are more ready to help siblings than friends. Finally, other studies showed that facial resemblances enhance trust (DeBruine, 2002). In each cases, we see a mechanisms whose function is to negotiate our investments in relationships in order to promote the copies of our genes housed in people who are, or look like, or could help us expand our kin. For instance, by simply viewing lingerie or picture of sexy women, men behave more fairly in the ultimatum game (Van den Bergh & Dewitte, 2006).

Many of these so-called altruistic behavior can be explained only by the operations of hyper-active agency detectors and a bias toward fearing other people’s judgement. When they are not being or feeling watched, peoples behave less altruistically. Many studies show that in the dictator game, a version of the ultimatum game where the responder has to accept the offer, subjects always make lower offers than in the ultimatum (Bolton et al., 1998). Offers are even lower in the dictator game when donation is fully anonymous (Hoffman et al., 1994). According to the present framework, it would be because there is no advantage in being fair.

When subjects feel watched, or think of agents, even supernatural ones, they tend to be much more altruistic. When a pair of eyes is displayed in a computer screen, almost twice as many participants transfer money in the dictator game (Haley & Fessler, 2005), and people contribute 3 times more in an honesty box for coffee' when there is a pair of eyes than when there is pictures of a flower (Bateson et al., 2006). The sole fact of speaking of ghosts enchances honest behavior in a competitive taks(Bering et al., 2005), while priming subjects with the God concept in the anonymous dictator game (Shariff & Norenzayan, in press).

These reflections also applies to altruistic punishment. First, it is enhanced by an audience. (Kurzban et al., 2007) showed that with a dozen participants, punishment expenditure tripled. In the trust game, players apply learned social rules and trust-building routines, but they hate when cheater enjoy what they themselves refrain from enjoying. Thus it feels good to reset the equilibrium. Again, appareant altruism is instrumental in personal satisfaction, at least in some occasions.

Hardy & Van Vugt, in their theory of competitive altruism suggest that

individuals attempt to outcompete each other in terms of generosity. It emerges because altruism enhances the status and reputation of the giver. Status, in turn, yields benefits that would be otherwise unattainable (Hardy & Van Vugt, 2006)

Maybe agents are attempting to maximize a complex hedonic utility function, where the reward and the losses can be monetary, emotional or social. A possible alternative approach is what I call ‘methodological hedonism’: let’s assume, at least for identifying cognitive mechanisms, that the brain, when in function normally, tries to maximize hedonic feelings, even in moral behavior. We use feelings to anticipate feelings in order to control our behavior toward a maximization of positive feelings and a minimization of negative ones. The ‘hot logic’ of emotions is more realist than the cold logic of traditional game theory but still preserve the idea of utility maximization (although “value” would be more appropriate). In this framework, altruistic behavior is possible, but need not to rely on altruistic cognition. Cognitive mechanisms of decision-making aims primarily at maximizing positive outcomes and minimizing negative ones. The initial hedonism is gradually modulated by social norms, by which agents learn how to maximize their utility given the norms. Luckily, however, biological and cultural evolution favored patterns of self-interest that promote social order to a certain extent: institutions, social norms, routines and cultures tend to structure morally our behavior. Thus understanding morality may amount to understand how individual’s egoism is modulated by social processes. There might be no need to posit an innate Strong Reciprocity. Or at least it is worth to explore other avenues!

Related posts

• Understanding two models of fairness: outcome-based inequity aversion vs. intention-based reciprocity
• Altruism: a research program

Suggested reading:

• Strong Reciprocity and the Emergence of Large-Scale Societies, by Benoît Dubreuil, forthcoming in Philosophy of the Social Sciences

Update:

I forgot to mention a thorough presentation and excellent criticism of Strong Reciprocity:

• Burnham, T. C., & Johnson, D. D. P. (2005). The Biological and Evolutionary Logic of Human Cooperation. Analyse & Kritik, 27, 113-135.

important papers from the Collective are:

• Bowles, S., & Gintis, H. (2004). The Evolution of Strong Reciprocity: Cooperation in Heterogeneous Populations. Theoretical Population Biology, 65(1), 17-28.
• Fehr, E., Fischbacher, U., & Gachter, S. (2002). Strong Reciprocity, Human Cooperation, and the Enforcement of Social Norms. Human Nature, 13(1), 1-25.
• Fehr, E., & Rockenbach, B. (2004). Human Altruism: Economic, Neural, and Evolutionary Perspectives. Curr Opin Neurobiol, 14(6), 784-790.
• Gintis, H. (2000). Strong Reciprocity and Human Sociality. Journal of Theoretical Biology, 206(2), 169-179.

References

• Bateson, M., Nettle, D., & Roberts, G. (2006). Cues of Being Watched Enhance Cooperation in a Real-World Setting. Biology Letters, 12, 412-414.
• Bering, J. M., McLeod, K., & Shackelford, T. K. (2005). Reasoning About Dead Agents Reveals Possible Adaptive Trends. Human Nature, 16(4), 360-381.
• Bolton, G. E., Katok, E., & Zwick, R. (1998). Dictator Game Giving: Rules of Fairness Versus Acts of Kindness International Journal of Game Theory, 27 269-299
• DeBruine, L. M. (2002). Facial Resemblance Enhances Trust. Proc Biol Sci, 269(1498), 1307-1312.
• Haley, K., & Fessler, D. (2005). Nobody’s Watching? Subtle Cues Affect Generosity in an Anonymous Economic Game. Evolution and Human Behavior, 26(3), 245-256.
• Hardy, C. L., & Van Vugt, M. (2006). Nice Guys Finish First: The Competitive Altruism Hypothesis. Pers Soc Psychol Bull, 32(10), 1402-1413.
• Hoffman, E., Mc Cabe, K., Shachat, K., & Smith, V. (1994). Preferences, Property Rights, and Anonymity in Bargaining Experiments. Games and Economic Behavior, 7, 346–380.
• Kurzban, R., DeScioli, P., & O'Brien, E. (2007). Audience Effects on Moralistic Punishment. Evolution and Human Behavior, 28(2), 75-84.
• Madsen, E. A., Tunney, R. J., Fieldman, G., Plotkin, H. C., Dunbar, R. I. M., Richardson, J.-M., & McFarland, D. (2007). Kinship and Altruism: A Cross-Cultural Experimental Study. British Journal of Psychology, 98, 339-359.
• Shariff, A. F., & Norenzayan, A. (in press). God Is Watching You: Supernatural Agent Concepts Increase Prosocial Behavior in an Anonymous Economic Game. Psychological Science.
• Stewart-Williams, S. (2007). Altruism among Kin Vs. Nonkin: Effects of Cost of Help and Reciprocal Exchange. Evolution and Human Behavior, 28(3), 193-198.
• Van den Bergh, B., & Dewitte, S. (2006). Digit Ratio (2d:4d) Moderates the Impact of Sexual Cues on Men's Decisions in Ultimatum Games. Proc Biol Sci, 273(1597), 2091-2095.

Neuroeconomics, folk-psychology, and eliminativism

conventional wisdom has long modeled our internal cognitive processes, quite wrongly, as just an inner version of the public arguments and justifications that we learn, as children, to construct and evaluate in the social space of the dinner table and the marketplace. Those social activities are of vital importance to our collective commerce, both social and intellectual, but they are an evolutionary novelty, unreflected in the brain’s basic modes of decision-making
(Churchland, 2006, p. 31).

The folk-psychological model of rationality construes rational decision-making as the product of a practical reasoning by which an agent infers, from her beliefs and desires, the right action to do. Truely, when we are asked to explain or predict actions, our intuitions lead us to describe them as the product of intentional states. In a series of studies, Malle and Knobe (Malle & Knobe, 1997, 2001) showed that folkpsychology is a language game where beliefs, desires and intentions are the main players. But using the intentional idiom does not mean that it picks out the real causes of action. This is where realist, instrumentalist and eliminativist accounts conflict. A realist account of beliefs and desires takes them to be real causal entities, an instrumentalist account treat them as useful fictions while an eliminativist account suggests that they are embedded in a faulty theory of mental functioning that should be eliminated (see Paul M. Churchland & Churchland, 1998; Dennett, 1987; Fodor, 1981). Can neuroeconomics shed light on this traditional debate in philosophy and cognitive science?

Neuroeconomics, I suggest, support an eliminativist approach of cognition. Just like contemporary chemistry does not explain combustion by a release of phlogiston (a substance supposed to exist in combustible bodies), cognitive science should stop explaining actions as the product of beliefs and desires. Behavioral regularities and neural mechanisms are sufficient to explain decision. When subjects evaluate whether or not they would buy a product, and whether or not the price seems justified, how informative is it to cite propositional attitudes as causes? The real entities involved in decision-makings are neural mechanisms involved in hedonic feelings, cognitive control, emotional modulation, conflict monitoring, planning, etc. Preferences, utility functions or practical reasoning, for instance, can explain purchasing, but they do not posit entities that can enter the “causal nexus” (Salmon, 1984). Neuroeconomics explains purchasing behavior not as an inference from beliefs-desire to action, but as a tradeoff, mediated by prefrontal areas, between the pleasure of acquiring (elicited in the nucleus accumbens) and the pain of purchasing (elicited in the insula). Prefrontal activation predicted purchasing, while insular activation predicted the decision of not purchasing (Knutson et al., 2007). Hence the explanation of purchasing cites causes (brain areas) that explain the purchasing behavior as the product of a higher activation in prefrontal area and that justifies the decision to purchase: the agents had a stronger incentive to buy. A fully mechanistic account would, of course, details the algorithmic process performed by each area.
The belief-desire framework implicitly supposes that the causes of an action are those that an agent would verbally express when asked to justify her action. But on what grounds can this be justified?

Psychological and neural studies suggest rather a dissociation between the mechanisms that lead to actions and the mechanisms by which we explain them. Since Nisbett & Wilson (1977) seminal studies, research in psychology showed that the very act of explaining the intentional causes of our actions is a re-constructive process that might be faulty. Subjects give numerous reasons as to why they prefer one pair of socks (or other objects) to another, but they all prefer the last one on the right. The real explanation of their preferences is a position effect, or right-hand bias. For some reason, subjects pick the right-hand pair and, post hoc, generate an explanation for this preference, a phenomena widely observed. For instance, when subjects tasted samples of Pepsi and Coke with and without the brand’s label, they reported different preferences (McClure et al., 2004). Without labels, subjects evaluate both drinks similarly. When drinks were labeled, subjects report a stronger preference for Coke, and neuroimaging studies mirrored this branding effect. Sensory information (taste) and cultural information (brand) are associated with different areas that interact so as to bias preferences. Without the label, the drink evaluation relies solely on sensory information. Subjects may motivate their preferences for one beverage over another with many diverse arguments, but the real impact on their preference is the brand’s label. The conscious narrative we produce when rationalizing our actions are not “direct pipeline[s] to nonconscious mental processes” (Wilson & Dunn, 2004, p. 507) but approximate reconstructions. When our thoughts occur before the action, when they are consistent with the action and appear as the only cause of the action, we infer that these thoughts are the causes of the actions, and rule out other internal or external causes (Wegner, 2002). But the fact that we rely on the belief-desire framework to explain our and others’ action as the product of intentional states do not constitute an argument for considering that these states are satisfying causal explanation of action.

The belief-desire framework might be a useful conceptual scheme for fast and frugal explanations, but it does not make folkpsychological constructs suitable for scientific explanation. In the same vein, if folkbiology would be the sole foundation of biology, whales would still be categorized as fish. The nature of the biological world is not explained by our (faulty and biased) folkbiology, but by making explicit the mechanism of natural selection, reproduction, cellullar growth, etc. There is no reason to believe that our folkpsychology is a better description of mental mechanisms. Beliefs, desires and intentions are folk-psychological constructs that have no counterpart in neuroscience. Motor control and action planning, for instance, are explained by different kinds of representation such as forward and inverse models, not propositional attitudes (Kawato & Wolpert, 1998; Wolpert & Kawato, 1998). Consequently, the fact that we rely on fokpsychology to explain actions does not constitute an argument for considering that this naïve theory provides reliable explanations of actions. Saying that the sun rises every morning is a good prediction, it could explains why there is more heat and light at noon, but the effectiveness of the sun-rising framework does not justifies its use as a scientific theory.

As many philosophers of science suggested, a genuine explanation is mechanistic: it consists in breaking a system in parts and process, and explaining how these parts and processes cause the system to behave the way it does (Bechtel & Abrahamsen, 2005; Craver, 2001; Machamer et al., 2000). Folkpsychology may save the phenomena, it still does not propose causal parts and processes. More generally, the problem with the belief-desire framework is that it is a description of our attitude toward things we call "agent", not a description of what constitutes the true nature of agents. Thus, it conflates the map and the territory. Moreover, conceptual advance is made when objects are described and classified according to their objective properties. A chemical theory that classifies elements according to their propensity to quench thirst would be a non-sense (although it could be useful in other context). At best, the belief-desire framework could be considered as an Everyday Handbook of Intentional Language.

References

• Bechtel, W., & Abrahamsen, A. (2005). Explanation: A Mechanist Alternative. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 36(2), 421-441.
• Churchland, P. M. (2006). Into the Brain: Where Philosophy Should Go from Here. Topoi, 25(1), 29-32.
• Churchland, P. M., & Churchland, P. S. (1998). On the Contrary : Critical Essays, 1987-1997. Cambridge, Mass.: MIT Press.
• Craver, C. F. (2001). Role Functions, Mechanisms, and Hierarchy. Philosophy of Science, 68, 53-74.
• Dennett, D. C. (1987). The Intentional Stance. Cambridge, Mass.: MIT Press.
• Fodor, J. A. (1981). Representations : Philosophical Essays on the Foundations of Cognitive Science (1st MIT Press ed.). Cambridge, Mass.: MIT Press.
• Kawato, M., & Wolpert, D. M. (1998). Internal Models for Motor Control. Novartis Found Symp, 218, 291-304; discussion 304-297.
• Knutson, B., Rick, S., Wimmer, G. E., Prelec, D., & Loewenstein, G. (2007). Neural Predictors of Purchases. Neuron, 53(1), 147-156.
• Machamer, P., Darden, L., & Craver, C. F. (2000). Thinking About Mechanisms. Philosophy of Science, 67, 1-24.
• Malle, B. F., & Knobe, J. (1997). The Folk Concept of Intentionality. Journal of Experimental Social Psychology, 33, 101-112.
• Malle, B. F., & Knobe, J. (2001). The Distinction between Desire and Intention: A Folk-Conceptual Analysis. In B. F. M. L. J. Moses & D. A. Baldwin (Eds.), Intentions and Intentionality: Foundations of Social Cognition (pp. 45-67). Cambridge, MA: MIT Press.
• McClure, S. M., Li, J., Tomlin, D., Cypert, K. S., Montague, L. M., & Montague, P. R. (2004). Neural Correlates of Behavioral Preference for Culturally Familiar Drinks. Neuron, 44(2), 379-387.
• Nisbett, R. E., & Wilson, T. D. (1977). Telling More Than We Can Know: Verbal Reports on Mental Processes. Psychological Review, 84, 231-259.
• Salmon, W. C. (1984). Scientific Explanation and the Causal Structure of the World. Princeton, N.J.: Princeton University Press.
• Wegner, D. M. (2002). The Illusion of Conscious Will. Cambridge, Mass.: MIT Press.
• Wilson, T. D., & Dunn, E. W. (2004). Self-Knowledge: Its Limits, Value, and Potential for Improvement. Annual Review of Psychology, 55(1), 493-518.
• Wolpert, D. M., & Kawato, M. (1998). Multiple Paired Forward and Inverse Models for Motor Control. Neural Networks, 11(7-8), 1317.

homepage down [decisis.net]

Dear readers,

please note that my homepage (www.decisis.net) is currently down (or on-and-off) due to my horrible web hosting provider (Centrale internet). DO NOT EVER buy a domain with them, they put the domain at their name, and hence you are no longer the owner of the domain. That is fraud. They offer a crappy service--when they reply--and will probably be sued for their fraudulent practices (people are organizing a class action). Hence I will be hosted soon somewhere else and will have another URL. This blog will not move, but links to my homepage will be down for a day or two. I'll communicate my new address soon.

NEVER do business with them. See these horror stories (in French):

• http://mediameriquat.com/node/22
• http://www.radio-canada.ca/radio/techno/commentaires-88212.shtml
• http://nathaliefortin.blogspot.com/2007/01/bye-bye-la-centrale-internet.html

Neuroeconomics with Jason Zweig (video)

Jason Zweig, author of "Your Money and Your Brain," discusses how of neuroeconomics can help financial decision-making. (from brightcove.tv)

New feature: leave your comments

You can now leave comments after each blog post. I think it would be a great way to interact or exchange ideas. However, certain rules apply:

1. Comments are moderated.
2. A word verification program (Captcha) will make sure that you are a human.
   
3. Any comment that:
   

• is abusive
• is off-topic
• contains ad-hominem attacks
• promotes hate of any kind
• uses excessively foul language
• is blatantly spam
• is too long
• is only self-promotion

may be edited or deleted (a more detailed policy may be found there).

You can read "Lifehacker's guide to weblog comments" for more details about blog comments netiquette.

Looking forward to reading you,
Benoit.

Rational performance and behavioral ecology

The oeconomy of nature is in this respect exactly of a piece with what it is upon many other occasions. With regard to all those ends which, upon account of their peculiar importance, may be regarded, if such an expression is allowable, as the favourite ends of nature, she has constantly in this manner not only endowed mankind with an appetite for the end which she proposes, but likewise with an appetite for the means by which alone this end can be brought about, for their own sakes, and independent of their tendency to produce it. Thus self-preservation, and the propagation of the species, are the great ends which Nature seems to have proposed in the formation of all animals. Mankind are endowed with a desire of those ends, and an aversion to the contrary; with a love of life, and a dread of dissolution; with a desire of the continuance and perpetuity of the species, and with an aversion to the thoughts of its intire extinction. But though we are in this manner endowed with a very strong desire of those ends, it has not been intrusted to the slow and uncertain determinations of our reason, to find out the proper means of bringing them about.

- Adam Smith, (1759)

One of the main goal of this blog (and the research that feeds it) is the development of a coherent and rigourous naturalized theory of rationality--or more precisely, a theory of natural rationality (I'll discuss the difference in another post). This theory-in-progress construes rationality as a natural feature of the biological world (and yes, fellows philosophers, I deal with the question of normativity, but another day). The big picture is the following: there is a distiction between rational competence and rational performance (as in linguistics): the competence is the set of mechanisms that make rational performance (= rational actions) possible. Neuroeconomics, as I see it, is the most promising research program that attempt to decipher the rational competence. An interesting possibility raised by these researches is that neural mechanisms involved in rational comptence may not be uniquely humans. In my phd thesis (in French, pdf here), I proposed that the whole vertebrate clade should be considered as the natural kind that implements the category "rational agents". I blogged a lot about neuroeconomics (competence), so today I'll talk about performance. How do animals behave rationally? In a basic, utility-maximizing sense: they optimize a utility function. While research in behavioral ecology showed that this hypothesis is justified, neureoconomics shows that it is more than an 'as-if' hypothesis or a useful fiction. So let's talk about behavioral ecology and rational performance.

Behavioral ecology models animals as economic agents that achieve ultimate goals (survival and reproduction) through instrumental ones (partner selection, food acquisition and consumption, etc.)[1]. Optimal foraging theory, for instance, represents foraging as a maximization of net caloric intake. With general principles derived from microeconomics, optimization theory and control theory, coupled with information about the physical constitution and ecological niche of the predator, it is possible to predict what kind of prey and patch an animal will favor, given certain costs such as search, identification, procurement, and handling costs. Optimal foraging theory (OFT), as their founders suggested, tries to determine “which patches a species would feed and which items would form its diet if the species acted in the most economical fashion”[2]. OFT models primarily animals as efficient goal-seekers and goal-achievers.
OFT thus incorporates agents, their choices, the currency to be maximized (most of the time a caloric gain) and a set of constraints. Most researches study where to forage (patch choice), what to forage (prey choice) and for how long (optimal time allocation). It is supposed that the individual animal make a series of decisions in order to solve a problem of sequential optimization. An animal looking for nutrients must maximize its caloric intake while taking into account those spent in seeking and capturing its prey; to this problem one must also add, among others, the frequency of prey encounter, the time devoted to research and the calories each prey type afford. All these parameters can be represented by a set of equations from which numerical methods such as dynamic programming allow biologists to derive algorithms that an optimal forager would implement in order to maximize the caloric intake. These algorithms are used afterward for the prediction of the behavior. Mathematically speaking, OFT is the translation of decision theory axioms—together with many auxiliary hypotheses—into tractable calories-maximization algorithms.
Economic models of animal behavior succeeded in explanation and prediction. It predicts for example how birds split their time between defending a territory and foraging[3], or between singing and foraging[4]. In their meta-analysis, Sih and Christensen[5] re-examined 134 foraging studies in laboratory and natural context, experimental or observational, and concluded that, although predictive success is not perfect, the predictivity of the theory is relatively high when preys are motionless (the prey can be a plant, seeds, honey, etc).
Interactive contexts are aptly modeled by game theory, mainly social foraging, fighting and predatory-preys relations[6]. For example, a model of Vickery et al[7] predicted that the co-occurrence of three social foraging strategies, producer (gathering nutrients) scrounger (stealing nutrients) and opportunist (switching between producer and scrounger) occurs only in the—very improbable—case where the losses opportunists would incur while foraging would be exactly equivalent to the profit of stealing. The model, however, predicts certain distributions of pairs of strategies that constitute evolutionary stable strategies (ESS), that is, a strategy that cannot be invaded by any competing alternative strategy. The proportion of food patch shared by scroungers, the size of the group and the degree of compatibility between the scrounger and producer strategy (i.e., if it is easy for the animal to perform both activities) determine the distribution of the strategies in a population, which was confirmed, inter alia, in birds (Lonchura punctulata)[8]. As predicted by the model, the producer strategy becomes less common when the cost of individual foraging increases.
Recently, behavioral ecologists found that animals could also be modeled as traders in biological markets. Obviously, biological markets do not have symbolic and conventional currencies systems, but in many interactions between animals institute trading structures. As soon as agents are able to provide commodities for mutual profit, the competition for obtaining commodities creates a higher bid. Animals seek and select partners according to the principle of supply and demand in interspecific mutualism, mate selection and intraspecific co-operation. An example of the last type is the cleaning market instituated by Hipposcarus harid fishes and cleaners-fishes Labroides dimidiatus. The “customers” (Hipposcarus) use the services of the cleaner to have its parasites removed, whereas the cleaners, occasionally, cheat and eat the healthy tissues of its customers. Since the cleaners offer a service that cannot be found elsewhere, they benefit from a certain economic advantage. A customer cannot choose to be exploited or not, whereas the cleaner chooses to cooperate or not (thus the payoffs are asymmetric). The customer—a predator fish that could eat the cleaner—abstains from consuming the cleaner in the majority of the cases, given the reciprocal advantage. Bshary and Schaffer[9] observed that cleaners spend more time with occasional customers than with regular ones and fight for them, since occasional customers are easier to exploit. All this makes perfect economic sense.
One could of course reformulate each of these results, and put the words decision or exchange between quotation marks to imply that they are mere façons de parler and not really decisions and exchanges, but instinctive behaviors preserved by natural selection. If this is the case, we should also put quotation marks when we talk about human beings: human behavioral ecology applies the same bio-economic logic with the same success to humans. Agents are modeled as optimal forager subjects to a multitude of constraints. Given available resources in the environment of a community, one can generates a model that predicts the optimal allocation of resources. These models are of course more complex than animal ones since they integrate social parameters like local habits, technology or economic structures. Models of human foraging where able for instance to explain differences in foraging style between tribes in the Amazonia, given the distance to be traversed and the technology used[10]. Food sharing, labor division between men and women, agricultural cultures and even Internet browsing (where the commodity is information) can be modeled by human behavioral ecology[11]. Hence even if foraging or trading behaviors are merely the execution of adaptations, the fact remains that their performance is best described as a decision-making process.


[1] (Krebs & Davies, 1997; Pianka, 2000)
[2] (MacArthur & Pianka, 1966, p. 603).
[3] (Kacelnik, Houston, & Krebs, 1981),
[4] (Thomas, 1999)
[5] (Sih & Christensen, 2001)
[6] (Hansen, 1986; Lima, 2002).(Dugatkin & Reeve, 1998)
[7] (Vickery, Giraldeau, Templeton, Kramer, & Chapman, 1991)
[8] (Mottley & Giraldeau, 2000)
[9] (Bshary & Schaffer, 2002)
[10] (Hames & Vickers, 1982)
[11] (Jochim, 1988; Kaplan, Hill, Hawkes, & Hurtado, 1984; Pirolli & Card, 1999)


Bshary, R., & Schaffer, D. (2002). Choosy reef fish select cleaner fish that provide high-quality service. Animal Behaviour, 63(3), 557.
Dugatkin, L. A., & Reeve, H. K. (1998). Game theory & animal behavior. New York ; Oxford: Oxford University Press.
Hames, R. B., & Vickers, W. T. (1982). Optimal Diet Breadth Theory as a Model to Explain Variability in Amazonian Hunting. American Ethnologist, 9(2, Economic and Ecological Processes in Society and Culture), 358-378.
Hansen, A. J. (1986). Fighting Behavior in Bald Eagles: A Test of Game Theory. Ecology, 67(3), 787-797.
Jochim, M. A. (1988). Optimal Foraging and the Division of Labor. American Anthropologist, 90(1), 130-136.
Kacelnik, A., Houston, A. I., & Krebs, J. R. (1981). Optimal foraging and territorial defence in the Great Tit (Parus major). Behavioral Ecology and Sociobiology, 8(1), 35.
Kaplan, H., Hill, K., Hawkes, K., & Hurtado, A. (1984). Food Sharing Among Ache Hunter-Gatherers of Eastern Paraguay. Current Anthropology, 25(1), 113-115.
Krebs, J. R., & Davies, N. B. (1997). Behavioural ecology : an evolutionary approach (4th ed.). Oxford, England ; Malden, MA: Blackwell Science.
Lima, S. L. (2002). Putting predators back into behavioral predator-prey interactions. Trends in Ecology & Evolution, 17(2), 70.
MacArthur, R. H., & Pianka, E. R. (1966). On optimal use of a patchy environment. American Naturalist(100), 603-609.
Mottley, K., & Giraldeau, L. A. (2000). Experimental evidence that group foragers can converge on predicted producer-scrounger equilibria. Anim Behav, 60(3), 341-350.
Pianka, E. R. (2000). Evolutionary ecology (6th ed.). San Francisco, Calif.: Benjamin Cummings.
Pirolli, P., & Card, S. (1999). Information Foraging. Psychological Review, 106(4), 643.
Sih, A., & Christensen, B. (2001). Optimal diet theory: when does it work, and when and why does it fail? Animal Behaviour, 61(2), 379.
Smith, A. ([1759] 2002). The theory of moral sentiments. Cambridge, U.K. ; New York: Cambridge University Press.
Thomas, R. J. (1999). Two tests of a stochastic dynamic programming model of daily singing routines in birds. Anim Behav, 57(2), 277-284.
Vickery, W. L., Giraldeau, L.-A., Templeton, J. J., Kramer, D. L., & Chapman, C. A. (1991). Producers, Scroungers, and Group Foraging. American Naturalist, 137(6), 847-863.

Natural Irrationality. How judgement and decision-making can go wrong

Lifehack has an interesting post about "7 Stupid Thinking Errors You Probably Make". Readers of this blog might be already familiar with these (confirmation bias, recency effects, etc.), so here a the full list from Wikipedia:

• Bandwagon effect — the tendency to do (or believe) things because many other people do (or believe) the same. Related to groupthink, herd behaviour, and manias.
• Base rate fallacy
• Bias blind spot — the tendency not to compensate for one's own cognitive biases.
• Choice-supportive bias — the tendency to remember one's choices as better than they actually were.
• Confirmation bias — the tendency to search for or interpret information in a way that confirms one's preconceptions.
• Congruence bias — the tendency to test hypotheses exclusively through direct testing, in contrast to tests of possible alternative hypotheses.
• Contrast effect — the enhancement or diminishment of a weight or other measurement when compared with recently observed contrasting object.
• Déformation professionnelle — the tendency to look at things according to the conventions of one's own profession, forgetting any broader point of view.
• Endowment effect — "the fact that people often demand much more to give up an object than they would be willing to pay to acquire it".
• Extreme aversion — the tendency to avoid extremes, being more likely to choose an option if it is the intermediate choice.
• Focusing effect — prediction bias occurring when people place too much importance on one aspect of an event; causes error in accurately predicting the utility of a future outcome.
• Framing — by using a too narrow approach or description of the situation or issue.
• Hyperbolic discounting — the tendency for people to have a stronger preference for more immediate payoffs relative to later payoffs, the closer to the present both payoffs are.
• Illusion of control — the tendency for human beings to believe they can control or at least influence outcomes that they clearly cannot.
• Impact bias — the tendency for people to overestimate the length or the intensity of the impact of future feeling states.
• Information bias — the tendency to seek information even when it cannot affect action.
• Irrational escalation — the tendency to make irrational decisions based upon rational decisions in the past or to justify actions already taken.
• Loss aversion — "the disutility of giving up an object is greater than the utility associated with acquiring it".(see also sunk cost effects and Endowment effect).
• Mere exposure effect — the tendency for people to express undue liking for things merely because they are familiar with them.
• Need for closure — the need to reach a veredict in important matters; to have an answer and to escape the feeling of doubt and uncertainty. The personal context (time or social pressure) might increase this bias.
• Neglect of probability — the tendency to completely disregard probability when making a decision under uncertainty.
• Omission bias — The tendency to judge harmful actions as worse, or less moral, than equally harmful omissions (inactions).
• Outcome bias — the tendency to judge a decision by its eventual outcome instead of based on the quality of the decision at the time it was made.
• Planning fallacy — the tendency to underestimate task-completion times.
• Post-purchase rationalization — the tendency to persuade oneself through rational argument that a purchase was a good value.
• Pseudocertainty effect — the tendency to make risk-averse choices if the expected outcome is positive, but make risk-seeking choices to avoid negative outcomes.
• Reactance - the urge to do the opposite of what someone wants you to do out of a need to resist a perceived attempt to constrain your freedom of choice.
• Selective perception — the tendency for expectations to affect perception.
• Status quo bias — the tendency for people to like things to stay relatively the same (see also Loss aversion and Endowment effect).
• Unit bias — the tendency to want to finish a given unit of a task or an item with strong effects on the consumption of food in particular
• Von Restorff effect — the tendency for an item that "stands out like a sore thumb" to be more likely to be remembered than other items.
• Zero-risk bias — preference for reducing a small risk to zero over a greater reduction in a larger risk.

New paper (in French): Des lois de la pensée au cerveau-machine.

Commentaires bienvenus ;-) !

Hardy-Vallée, B. (forthcoming) Des lois de la pensée au cerveau-machine. In Informatique et Sciences Cognitives : Influences ou Confluences?, Paris, Ophrys/MSH, D. Kayser et C. Garbay (eds).

A plea for interdisciplinarity (Hayek's quote)

he who is only an economist cannot be a good economist. Much more than in the natural sciences, it is true in the social sciences that there is hardly a concrete problem which can be adequately answered on the basis of a single special discipline.

-- Hayek, 1967

p. 267 in Studies in Philosophy, Politics and Economics, pp.251–269. Chicago: University of Chicago Press.

Orbitofrontal Cortex Encodes Willingness to Pay in Everyday Economic Transactions

Last week, I discussed the confusion around the notion of valuation. Just to add a little complexity, a new study shows that OFC (also thought to encode economic value) encore the willingness-to-pay:

An essential component of every economic transaction is a willingness-to-pay (WTP) computation in which buyers calculate the maximum amount of financial resources that they are willing to give up in exchange for the object being sold. Despite its pervasiveness, little is known about how the brain makes this computation. We investigated the neural basis of the WTP computation by scanning hungry subjects' brains using functional magnetic resonance imaging while they placed real bids for the right to eat different foods. We found that activity in the medial orbitofrontal cortex and in the dorsolateral prefrontal cortex encodes subjects' WTP for the items. Our results support the hypothesis that the medial orbitofrontal cortex encodes the value of goals in decision making.

• Plassmann, H., O'Doherty, J., & Rangel, A. (2007). Orbitofrontal Cortex Encodes Willingness to Pay in Everyday Economic Transactions. J. Neurosci., 27(37), 9984-9988. Retrieved September 13, 2007, from

Philosophy of neuroscience: two recent papers

Sometimes, philosophers have relevant things to say about science. Here is two papers by two philosophers of (neuro)science I warmly recommend. The first one, by Michael Anderson, proposes a methodology to understand the contribution of different brain areas to cognitive functions (and to make sense of all these studies that say "area X does Z"):

Anderson, M.L. Massive redeployment, exaptation, and the functional integration of cognitive operations. Synthese, forthcoming.

The massive redeployment hypothesis (MRH) is a theory about the functional topography of the human brain, offering a middle course between strict localization on the one hand, and holism on the other. Central to MRH is the claim that cognitive evolution proceeded in a way analogous to component reuse in software engineering, whereby existing components—originally developed to serve some specific purpose—were used for new purposes and combined to support new capacities, without disrupting their participation in existing programs. If the evolution of cognition was indeed driven by such exaptation, then we should be able to make some specific empirical predictions regarding the resulting functional topography of the brain. This essay discusses three such predictions, and some of the evidence supporting them. Then, using this account as a background, the essay considers the implications of these findings for an account of the functional integration of cognitive operations. For instance, MRH suggests that in order to determine the functional role of a given brain area it is necessary to consider its participation across multiple task categories, and not just focus on one, as has been the typical practice in cognitive neuroscience. This change of methodology will motivate (even perhaps necessitate) the development of a new, domain-neutral vocabulary for characterizing the contribution of individual brain areas to larger functional complexes, and direct particular attention to the question of how these various area roles are integrated and coordinated to result in the observed cognitive effect. Finally, the details of the mix of cognitive functions a given area supports should tell us something interesting not just about the likely computational role of that area, but about the nature of and relations between the cognitive functions themselves. For instance, growing evidence of the role of “motor” areas like M1, SMA and PMC in language processing, and of “language” areas like Broca’s area in motor control, offers the possibility for significantly reconceptualizing the nature both of language and of motor control.

In the other paper, Chris Eliasmith presents the Engineering Framework (NEF), a simulation methodology.

Eliasmith, C. How to build a brain: from function to implementation. Synthese, forthcoming.

To have a fully integrated understanding of neurobiological systems, we must address two fundamental questions: 1. What do brains do (what is their function)? and 2. How do brains do whatever it is that they do (how is that function implemented)? I begin by arguing that these questions are necessarily inter-related. Thus, addressing one without consideration of an answer to the other, as is often done, is a mistake. I then describe what I take to be the best available approach to addressing both questions. Specifically, to address 2, I adopt the Neural Engineering Framework (NEF) of Eliasmith & Anderson [Neural engineering: Computation representation and dynamics in neurobiological systems. Cambridge, MA: MIT Press, 2003] which identifies implementational principles for neural models. To address 1, I suggest that adopting statistical modeling methods for perception and action will be functionally sufficient for capturing biological behavior. I show how these two answers will be mutually constraining, since the process of model selection for the statistical method in this approach can be informed by known anatomical and physiological properties of the brain, captured by the NEF. Similarly, the application of the NEF must be informed by functional hypotheses, captured by the statistical modeling approach.

Together, these two papers provides methodologies that contribute to a better understanding of the brain, its functions and its modelling. Check also their homepage for great papers on philosophy of neuroscience.

Links:

• Bickle, John and Peter Mandik and Anthony Landreth, "The Philosophy of Neuroscience", The Stanford Encyclopedia of Philosophy (Spring 2006 Edition), Edward N. Zalta (ed.).
• Chris Eliasmith
• Michael Anderson
• Philosophy of neuroscience resources. [highly recommended!!]
• Pete Mandik's Neurophilosophy Resources [highly recommended!!]
•