On Fairness and Tax

A colleague of mine send me this great blog post from The Economist's View, about fairness and tax. The bottom line: "the perception of fairness matters"

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.

A neuroecomic picture of valuation

Values are everywhere: ethics, politics, economics, law, for instance, all deal with values. They all involve socially situated decision-makers compelled to make evaluative judgment and act upon them. These spheres of human activity constitute three aspects of the same problem, that is, guessing how 'good' is something. This means that value 'runs' on valuation mechanisms. I suggest here a framework to understand valuation.

I define here valuation as the process by which a system maps an object, property or event X to a value space, and a valuation mechanism as the device implementing the matching between X and the value space. I do not suggest that values need to be explicitly represented as a space: by valuation space, I mean an artifact that accounts for the similarity between values by plotting each of them as a point in a multidimensional coordinate system. Color spaces, for instance, are not conscious representation of colors, but spatial depiction of color similarity along several dimensions such as hue, saturation brightness.

The simplest, and most common across the phylogenetic spectrum, value space has two dimensions: valence (positive or negative) and magnitude. Valence distinguishes between things that are liked and things that are not. Thus if X has a negative valence, it does not implies that X will be avoided, but only that it is disliked. Magnitude encodes the level of liking vs. disliking. Other dimensions might be added—temporality (whether X is located in the present, past of future), other- vs. self-regarding, excitatory vs. inhibitory, basic vs. complex, for instance—but the core of any value system is valence and magnitude, because these two parameters are required to establish rankings. To prefer Heaven to Hell, Democrats to Republicans, salad to meat, or sweet to bitter involves valence and magnitude.

Nature endowed many animals (mostly vertebrates) with fast and intuitive valuation mechanisms: emotions.[1] Although it is a truism in psychology and philosophy of mind and that there is no crisp definition of what emotions are[2], I will consider here that an emotion is any kind of neural process whose function is to attribute a valence and a magnitude to something else and whose operative mode are somatic markers. Somatic markers[3] are bodily states that ‘mark’ options as advantageous/disadvantageous, such as skin-conductance, cardiac rhythm, etc. Through learning, bodily states become linked to neural representations of the stimuli that brought these states. These neural structures may later reactivate the bodily states or a simulation of these states and thereby indicate the valence and magnitude of stimuli. These states may or may not account for many legitimate uses of the word “emotions”, but they constitute meaningful categories that could identify natural kinds[4]. In order to avoid confusion between folk-psychological and scientific categories, I will rather talk of affects and affective states, not emotions.

More than irrational passions, affectives states are phylogenetically ancient valuation mechanisms. Since Darwin,[5] many biologists, philosophers and psychologists[6] have argued that they have adaptive functions such as focusing attention and facilitating communication. As Antonio Damasio and his colleagues discovered, subjects impaired in affective processing are unable to cope with everyday tasks, such as planning meetings[7]. They lose money, family and social status. However, they were completely functional in reasoning or problem-solving tasks. Moreover, they did not felt sad for their situation, even if they perfectly understood what “sad” means, and seemed unable to learn from bad experiences. They were unable to use affect to aid in decision-making, a hypothesis that entails that in normal subjects, affect do aid in decision-making. These findings suggest that decision-making needs affect, not as a set of convenient heuristics, but as central evaluative mechanisms. Without affects, it is possible to think efficiently, but not to decide efficiently (affective areas, however, are solicited in subjects who learn to recognize logical errors[8]).

Affects, and specially the so-called ‘basic’ or ‘core’ ones such as anger, disgust, liking and fear[9] are prominent explanatory concepts in neuroeconomics. The study of valuation mechanisms reveals how the brain values certain objects (e.g. money), situations (e.g. investment, bargaining) or parameter (risk, ambiguity) of an economic nature. Three kinds of mechanisms are typically involved in neuroeconomic explanations:

1. Core affect mechanisms, such as fear (amygdala), disgust (anterior insula) and pleasure (nucleus accumbens), encode the magnitude and valence of stimuli.
2. Monitoring and integration mechanisms (ventromedial/mesial prefrontal, orbitofrontal cortex, anterior cingulate cortex) combine different values and memories of values together
3. Modulation and control mechanisms (prefrontal areas, especially the dorsolateral prefrontal cortex), modulate or even override other affect mechanisms.

Of course, there is no simple mapping between psychological functions and neural structures, but cognitive and affective neuroscience assume a dominance and a certain regularity in functions. Disgust does not reduce to insular activation, but anterior insula is significantly involved in the physiological, cognitive and behavioral expressions of disgust. There is a bit of simplification here—due to the actual state of science—but enough to do justice to our best theories of brain functioning. I will here review two cases of individual and strategic decision-making, and will show how affective mechanisms are involved in valuation[10].

In a study by Knutson et al.,[11] subjects had to choose whether or not they would purchase a product (visually presented), and then whether or not they would buy it at a certain price. While desirable products caused activation in the nucleus accumbens, activity is detected in the insula when the price is seen as exaggerated. If the price is perceived as acceptable, a lower insular activation is detected, but mesial prefrontal structures are more solicited. The activation in these areas was a reliable predictor of whether or not subjects would buy the product: prefrontal activation predicted purchasing, while insular activation predicted the decision of not purchasing. Thus purchasing decision involves 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). A chocolate box—a stimulus presented to the subjects—is located in the high-magnitude, positive-valence regions of the value space, while the same chocolate box priced at $80 is located in the high-magnitude, negative-valence regions of the space.

In the ultimatum game, a ‘proposer’ makes an offer to a ‘responder’ that can either accept or refuse the offer. The offer is a split of an amount of money. If the responder accepts, she keeps the offered amount while the proposer keeps the difference. If she rejects it, however, both players get nothing. Orthodox game theory recommends that proposers offer the smallest possible amount, while responder should accept every proposition, but all studies confirm that subjects make fair offer (about 40% of the amount) and reject unfair ones (less than 20%)[12]. Brain scans of people playing the ultimatum game indicate that unfair offers trigger, in the responders’ brain, a ‘moral disgust’: the anterior insula is more active when unfair offers are proposed,[13] and insular activation is proportional to the degree of unfairness and correlated with the decision to reject unfair offers[14]. Moreover, unfair offers are associated with greater skin conductance[15]. Visceral and insular responses occur only when the proposer is a human: a computer does not elicit those reaction. Beside the anterior insula, two other areas are recruited in ultimatum decisions: the dorsolateral prefrontal cortex (DLPFC). When there is more activity in the anterior insula than in the DLPFC, unfair offers tend to be rejected, while they tend to be accepted when DLPFC activation is greater than anterior insula.

These two experiments illustrate how neuroeconomics begin to decipher the value spaces and how valuation relies on affective mechanisms. Although human valuation is more complex than the simple valence-magnitude space, this ‘neuro-utilitarist’ framework is useful for interpreting imaging and behavioral data: for instance, we need an explanation for insular activation in purchasing and ultimatum decision, and the most simple and informative, as of today, is that it trigger a simulated disgust. More generally, it also reveals that the human value space is profoundly social: humans value fairness and reciprocity. Cooperation[16] and altruistic punishment[17] (punishing cheaters at a personal cost when the probability of future interactions is null), for instance, activate the nucleus accumbens and other pleasure-related areas. People like to cooperate and make fair offers.

Neuroeconomic experiments also indicate how value spaces can be similar across species. It is known for instance that in humans, losses[18] elicit activity in fear-related areas such as amygdala. Since capuchin monkey’s behavior also exhibit loss-aversion[19] (i.e., a greater sensitivity to losses than to equivalent gains), behavioral evidence and neural data suggests that the neural implementation of loss-aversion in primates shares common valuation mechanisms and processing. The primate—and maybe the mammal or even the vertebrate—value space locate loss in a particular region.

Notes

• [1] (Bechara & Damasio, 2005; Bechara et al., 1997; Damasio, 1994, 2003; LeDoux, 1996; Naqvi et al., 2006; Panksepp, 1998)
• [2] (Faucher & Tappolet, 2002; Griffiths, 2004; Russell, 2003)
• [3] (Bechara & Damasio, 2005; Damasio, 1994; Damasio et al., 1996)
• [4] (Griffiths, 1997)
• [5] (Darwin, 1896)
• [6] (Cosmides & Tooby, 2000; Paul Ekman, 1972; Griffiths, 1997)
• [7] (Damasio, 1994)
• [8] (Houde & Tzourio-Mazoyer, 2003)
• [9] (Berridge, 2003; P. Ekman, 1999; Griffiths, 1997; Russell, 2003; Zajonc, 1980)
• [10] The material for this part is partly drawn from (Hardy-Vallée, forthcoming)
• [11] (Knutson et al., 2007).
• [12] (Oosterbeek et al., 2004)
• [13] (Sanfey et al., 2003)
• [14] (Sanfey et al., 2003: 1756)
• [15] (van 't Wout et al., 2006)
• [16] (Rilling et al., 2002).
• [17] (de Quervain et al., 2004).
• [18] (Naqvi et al., 2006)
• [19] (Chen et al., 2006)

References

• Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52(2), 336.
• Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997). Deciding Advantageously Before Knowing the Advantageous Strategy. Science, 275(5304), 1293-1295.
• Berridge, K. C. (2003). Pleasures of the brain. Brain and Cognition, 52(1), 106.
• Chen, M. K., Lakshminarayanan, V., & Santos, L. (2006). How Basic Are Behavioral Biases? Evidence from Capuchin Monkey Trading Behavior. Journal of Political Economy, 114(3), 517-537.
• Cosmides, L., & Tooby, J. (2000). Evolutionary psychology and the emotions. Handbook of Emotions, 2, 91-115.
• Damasio, A. R. (1994). Descartes' error : emotion, reason, and the human brain. New York: Putnam.
• Damasio, A. R. (2003). Looking for Spinoza : joy, sorrow, and the feeling brain (1st ed.). Orlando, Fla. ; London: Harcourt.
• Damasio, A. R., Damasio, H., & Christen, Y. (1996). Neurobiology of decision-making. Berlin ; New York: Springer.
• Darwin, C. (1896). The expression of the emotions in man and animals ([Authorized ed.). New York,: D. Appleton.
• de Quervain, D. J., Fischbacher, U., Treyer, V., Schellhammer, M., Schnyder, U., Buck, A., et al. (2004). The neural basis of altruistic punishment. Science, 305(5688), 1254-1258.
• Ekman, P. (1972). Emotion in the human face: guide-lines for research and an integration of findings. New York,: Pergamon Press.
• Ekman, P. (1999). Basic emotions. In T. Dalgleish & M. Power (Eds.), Handbook of Cognition and Emotion (pp. 45-60). Sussex John Wiley & Sons, Ltd.
• Faucher, L., & Tappolet, C. (2002). Fear and the focus of attention. Consciousness & emotion, 3(2), 105-144.
• Griffiths, P. E. (1997). What emotions really are : the problem of psychological categories. Chicago, Ill.: University of Chicago Press.
• Griffiths, P. E. (2004). Emotions as Natural and Normative Kinds. Philosophy of Science, 71, 901–911.
• Hardy-Vallée, B. (forthcoming). Decision-making: a neuroeconomic perspective. Philosophy Compass.
• Houde, O., & Tzourio-Mazoyer, N. (2003). Neural foundations of logical and mathematical cognition. Nature Reviews Neuroscience, 4(6), 507-514.
• Knutson, B., Rick, S., Wimmer, G. E., Prelec, D., & Loewenstein, G. (2007). Neural predictors of purchases. Neuron, 53(1), 147-156.
• LeDoux, J. E. (1996). The emotional brain : the mysterious underpinnings of emotional life. New York: Simon & Schuster.
• Naqvi, N., Shiv, B., & Bechara, A. (2006). The Role of Emotion in Decision Making: A Cognitive Neuroscience Perspective. Current Directions in Psychological Science, 15(5), 260-264.
• Oosterbeek, H., S., R., & van de Kuilen, G. (2004). Differences in Ultimatum Game Experiments: Evidence from a Meta-Analysis. Experimental Economics 7, 171-188.
• Panksepp, J. (1998). Affective neuroscience : the foundations of human and animal emotions. New York: Oxford University Press.
• Rilling, J. K., Gutman, D. A., Zeh, T. R., Pagnoni, G., Berns, G. S., & Kilts, C. D. (2002). A Neural Basis for Social Cooperation. Neuron, 35(2), 395-405.
• Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychol Rev, 110(1), 145-172.
• Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-making in the Ultimatum Game. Science, 300(5626), 1755-1758.
• van 't Wout, M., Kahn, R. S., Sanfey, A. G., & Aleman, A. (2006). Affective state and decision-making in the Ultimatum Game. Exp Brain Res, 169(4), 564-568.
• Zajonc, R. B. (1980). Feeling and thinking: Preferences need no inferences. American Psychologist, 35(2), 151-175.
• 
  

The Economy of Nature: A Brief Introduction

all organic beings are striving to seize on each place in the economy of nature

(Darwin, [1859] 2003, p. 90)

Needless to say, Darwin’s theory of descent with modification was a true conceptual revolution[1]. It provides a general mechanism that explains the diversity and adaptivity of living beings—natural selection—and a guiding principle for organizing the mass of facts about them, the tree of life [2]. In recalling how this idea had come to his mind, Darwin wrote that he was trying to solve one problem: how is it that plants and animals sharing a common ancestry end up to be so different?

“The solution, as I believe, is that the modified offspring of all dominant and increasing forms tend to become adapted to many and highly diversified places in the economy of nature”[3].

Darwin repeatedly uses the expression “economy of nature” in The Origin of Species and other writings. He was not the first to conceive nature as an economy, although he was among the first to suggest an explicit similarity between natural and political economy. Before Darwin, the idea of nature as an economy had no particular ramification with human economic practices. In The Sacred Theory of the Earth, theologian Thomas Burnet referred to the “Oeconomy of nature” as the “well ordering of the great Family of living Creatures”[4] an order of divine origin. Swedish naturalist Carl Linnaeus, in his Specimen Academicum de Oeconomia Naturae, construed this divine order has being self-organized, exhibiting a balance of births and deaths, a complementarity between the function and purpose of life forms[5]. Adam Smith recognized the unity of this economy, where all living forms strive for “self-preservation, and the propagation of the species” but are limited in their problem-solving capacities, and hence must often rely on intuition instead of reasoning[6]. Lyell, in his Principle of Geology, describes how the involuntary agency of human and other animals “

contribute to extend or limit the geographical range and numbers of certain species, in obedience to general rules in the economy of nature, which are for most the part out of our control”[7].

Where Linnaeus saw a clockwork organization, Lyell’s representation of the world was more that of a dynamic equilibrium.

Thus, from natural theology to geology, the economy of nature referred to the complex organization of the universe[8]. What was new with Darwin is that the economy of nature began to be understood with conceptual tools borrowed from political economy. The division of labor, competition (“struggle” in Darwin’s words), trading, cost, the accumulation of innovations, the emergence of complex order from unintentional individual actions, the scarcity of resources and the geometric growth of populations are ideas borrowed from Adam Smith, Thomas Malthus, David Hume and other founders of modern economics. Thus, the “economy of nature” ceased to be an abstract representation of the universe and became a depiction of the complex web of interactions between biological individuals, species and their environment. The beginning of evolutionary biology coincided also with the beginning of ecology. The founder of ecology, Ernst Haeckel, defined this science as

“the body of knowledge concerning the economy of nature (…) the study of all those complex interrelationships referred to by Darwin as the condition of the struggle for existence”[9].

Consequently, Darwin’s main contributions are its transforming biology into a historical science (like geology) and an economic science[10]. From evolutionary game theory to biological markets, this approach is now flourishing.



Notes and References

[1] (Charles Darwin, 1859)
[2] (see Dennett, 1995; Gayon, 2003; Thagard, 1992, chapter 6)
[3] (C. Darwin, 1887, p. 84)
[4] (Burnet, [ca. 1692]1965, II, x),
[5] (Hestmark, 2000; Linnaeus, 1751).
[6] (Smith, [1759] 2002, p. 90)
[7] (Lyell, [1830-33]1853, p. 664)
[8] (Bowler, 1976; Ghiselin, 1978, 1995, 1999; Hammerstein & Hagen, 2005; Hodgson, 2001; Schabas, 2005)
[9] in “Morphology of Organisms” (1866); see (Stauffer, 1960).
[10] (Ghiselin, 1999, p. 7).

• Bowler, P. J. (1976). Malthus, Darwin, and the Concept of Struggle. Journal of the History of Ideas, 37(4), 631-650.
• Burnet, T. ([ca. 1692]1965). The sacred theory of the earth. Carbondale,: Southern Illinois University Press.
• Darwin, C. (1859). On the origin of species by means of natural selection. London,: J. Murray.
• Darwin, C. (1887). Autobiography. In F. Darwin (Ed.), The life and letters of Charles Darwin, including an autobiographical chapter (Vol. 1, pp. 26-106). London: John Murray.
• Dennett, D. C. (1995). Darwin's dangerous idea : evolution and the meanings of life. New York: Simon & Schuster.
• Gayon, J. (2003). From Darwin to Today in Evolutionary Biology. In J. Hodge & G. Radick (Eds.), The Cambridge Companion to Darwin (pp. 240-264). Cambridge: Cambridge University Press.
• Ghiselin, M. T. (1978). The Economy of the body The America Economic Review, 68 (2), 233-237.
• Ghiselin, M. T. (1995). Perspective: Darwin, progress and economic principle. Evolution, 49(6), 1029-1037.
• Ghiselin, M. T. (1999). Darwinian monism: the economy of nature. In P. Koslowski (Ed.), Sociobiology and bioeconomics : the theory of evolution in biological and economic theory (pp. x, 341 p.). Berlin ; New York: Springer.
• Hammerstein, P., & Hagen, E. H. (2005). The second wave of evolutionary economics in biology. Trends in Ecology & Evolution, 20(11), 604.
• Hestmark, G. (2000). Oeconomia Naturae L. Nature, 405(6782), 19.
• Hodgson, G. M. (2001). Bioeconomics. In P. A. O'Hara (Ed.), Encyclopedia of Political Economy (pp. 37-41). London ; New York: Routledge/Taylor & Francis Group.
• Linnaeus, C. (1751). Specimen Academicum de Oeconomia Naturae. Amoenitas Academicae, 2, 1-58.
• Lyell, C. ([1830-33]1853). Principles of geology; or, The modern changes of the earth and its inhabitants considered as illustrative of geology (9th and entirely rev. ed.). London,: J. Murray.
• Schabas, M. (2005). The natural origins of economics. Chicago: University of Chicago Press.
• Smith, A. ([1759] 2002). The theory of moral sentiments. Cambridge, U.K. ; New York: Cambridge University Press.
• Stauffer, R. C. (1960). Ecology in the Long Manuscript Version of Darwin's" Origin of Species" and Linnaeus'" Oeconomy of Nature". Proceedings of the American Philosophical Society, 104(2), 235-241.
• Thagard, P. (1992). Conceptual revolutions. Princeton, N.J.: Princeton University Press.

Greed—for lack of a better word—is not necessarily good: the other Adam Smith and the economics of altruism

In one of the most quoted passage of the Wealth of Nations, Adam Smith argue that economic self-interest leads to collective optima:

It is not from the benevolence of the butcher, the brewer or the baker that we expect our dinner, but from their regard to their own interest. We address ourselves not to their humanity but to their self-love, and never talk to them of our necessities but of their advantages. Nobody but a beggar chooses to depend chiefly upon the benevolence of their fellow-citizens.

Everybody will remember the famous Gordon Gekko's speech in Oliver Stone's Wall Street (1987):

The point is, ladies and gentlemen, that greed—for lack of a better word—is good. Greed is right. Greed works. Greed clarifies, cuts through, and captures the essence of the evolutionary spirit. Greed, in all of its forms—greed for life, for money, for love, knowledge—has marked the upward surge of mankind.

Things may not be that simple. While the standard Homo Economicus model represents agents as exclusively motivated by their material self-interest, economic theories of fairness put forth the picture of Homo Reciprocans, an agent whose utility function incorporates social parameters (Bowles & Gintis, 2002; see Fehr and Schmidt, 2003, for a review). Economic theories of fairness fall into two categories: outcome-based models and intention-based models. The former explains fairness as the product of players’ aversion to inequity (Bolton and Ockenfels, 2000; Fehr and Schmidt, 1999; see also this post). Players are sensible to the distributive consequences of strategic interactions and prefer resources allocations that reduce inequity: they negatively value a discrepancy between their own payoff and an equitable payoff (whether it’s the mean payoff or another player’s payoff). The latter explains fairness as the product of players’ reciprocation of perceived kindness or unkindness (Rabin, 1993 ; Dufwenberg & Kirchsteiger, 2004).). More than the outcome of an interaction, fairness is motivated by the attributed intention. For instance, in a ultimatum where the proposer’s behavior is restricted to two options (50/50 and 80/20 split), the second option is the most rejected; when the proposer’s options are 20/80 and 80/20, however, the first option is rejected less often (Falk, Fehr & Fischbacher 2003). Decision-makers value differently the same option whether it is perceived as an intention to be fair (valued positively) or not (negatively). Since both parameters appear to be important, many models integrate both intentions and outcomes (Fehr & Schmidt, 2003; Falk & Fischbacher, 2006).
A common feature of these models is the preservation of the optimality assumption: although they all suggest that standard utility function should incorporate different parameters, they do not reject the idea that agents are internally rational: they maximize a non-classical utility function.

Hence it is not surprising that contemporary research is more interested by the "first" Adam Smith, who wrote in the Theory of Moral Sentiments:

How selfish soever man may be supposed, there are evidently some principles in his nature, which interest him in the fortune of others, and render their happiness necessary to him, though he derives nothing from it except the pleasure of seeing it. Of this kind is pity or compassion, the emotion which we feel for the misery of others, when we either see it, or are made to conceive it in a very lively manner. That we often derive sorrow from the sorrow of others, is a matter of fact too obvious to require any instances to prove it; for this sentiment, like all the other original passions of human nature, is by no means confined to the virtuous and humane, though they perhaps may feel it with the most exquisite sensibility. The greatest ruffian, the most hardened violator of the laws of society, is not altogether without it.

In "Adam Smith, Behavioral Economist", Ashraf et al. (2005, The Journal of Economic Perspectives, 19, 131-145) discusses the relevance of Smith for experimental economics. In "The Two Faces of Adam Smith" (Southern Economic Journal, 65, 1-19), another Smith (Vernon) analyses the dual nature of Smith's (Adam) writing.

Finally, (found thanks to Mind Hacks) there is an excellent paper in the last Scientific American on the economics of fairness and other moral sentiments:

Is Greed Good?
Economists are finding that social concerns often trump selfishness in financial decision making, a view that helps to explain why tens of millions of people send money to strangers they find on the Internet
By Christoph Uhlhaas

There will be a conference a conference to commemorate the 250th anniversary of The Theory of Moral Sentiments in 2009 in Oxford (see CFP on PhilEcon website).

References

• Ashraf, N., Camerer, C. F., & Loewenstein, G. (2005). Adam Smith, Behavioral Economist. The Journal of Economic Perspectives, 19, 131-145.
  
• Bolton, G. E., & Ockenfels, A. (2000). ERC: A Theory of Equity, Reciprocity, and Competition. The American Economic Review, 90(1), 166-193.
• Bowles, S., & Gintis, H. (2002). Behavioural science: Homo reciprocans. Nature, 415(6868), 125-128.
• Bowles, S., & Gintis, H. (2004). The evolution of strong reciprocity: cooperation in heterogeneous populations. Theoretical Population Biology, 65(1), 17-28.
• Dufwenberg, M., & Kirchsteiger, G. (2004). A theory of sequential reciprocity. Games and Economic Behavior, 47(2), 268-298.
• Falk, A., Fehr, E., & Fischbacher, U. (2003). On the Nature of Fair Behavior. Economic Inquiry, 41(1), 20-26.
• Falk, A., & Fischbacher, U. (2006). A theory of reciprocity. Games and Economic Behavior, 54(2), 293-315.
• 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., & 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.
• Fehr, E., & Schmidt, K. (2003). Theories of Fairness and Reciprocity – Evidence and Economic Applications. In M. Dewatripont, L. Hansen & S. Turnovsky (Eds.), Advances in Economics and Econometrics - 8th World Congress (pp. 208-257).
• Fehr, E., & Schmidt, K. M. (1999). A Theory Of Fairness, Competition, and Cooperation. Quarterly Journal of Economics, 114(3), 817-868.
• Rabin, M. (1993). Incorporating Fairness into Game Theory and Economics. The American Economic Review, 83(5), 1281-1302.
• Smith, V. L. (1998). The Two Faces of Adam Smith. Southern Economic Journal, 65, 1-19.
  

Understanding two models of fairness: outcome-based inequity aversion vs. intention-based reciprocity

Why are people fair? Theoretical economics provides two generic models that fits the data. According to the first, inequity aversion, people are inequity-averse: they don't like a situation where one agent is disadvantaged over another. This model is based on consequences. The other model is based on intentions: although the consequences of an action are important, what matters here is the intention that motivates the action. I won't discuss which approach is better (it is an ongoing debates in economics), but I just wanted to share with a extremely clear presentations of the two parties, found in van Winden, F. (2007). Affect and Fairness in Economics. Social Justice Research, 20(1), 35-52., on pages 38-39:

In inequity aversion models (Bolton and Ockenfels, 2000; Fehr and Schmidt, 1999), which focus on the outcomes or payoffs of social interactions, any deviation between an individual's payoff and the equitable payoff (e.g., the mean payoff or the opponent's payoff) is supposed to be negatively valued by that individual. More formally, the crucial difference between an outcome-based inequity aversion model and the homo economicus model is that, in addition to the argument representing the individual's own payoff, a new argument is inserted in the utility function showing the individual's inequity aversion (social preferences), as in the social utility model (see, e.g., Handgraaf et al., 2003; Loewenstein et al., 1989; Messick and Sentis, 1985). The individual is then assumed to maximize this adapted utility function.

In intention-based reciprocity models it is not the outcomes of the interaction as such that matter, but the intentions of the players (Rabin, 1993; see also Falk and Fischbacher, 2006). The idea is that people want to reciprocate perceived (un)kindness with (un)kindness, because this increases their utility. Obviously, beliefs play a crucial role here. More formally, in this case, in addition to an individual's own payoff a new argument is inserted in the utility function incorporating the assumed reciprocity motive. As a consequence, if someone is perceived as being kind it increases the individual's utility to reciprocate with being kind to this other person. Similarly, if the other is believed to be unkind, the individual is better off by being unkind as well, because this adds to her or his utility. Again, this adapted utility function is assumed to be maximized by the individual.

Special issues of NYAS on biological decision-making

The may issue of the Annals of the New York Academy of Sciences is devoted to Reward and Decision Making in Corticobasal Ganglia Networks. Many big names in decision neuroscience (Berns, Knutson, Delgado, etc.) contributed.


Introduction. Current Trends in Decision Making
Bernard W Balleine, Kenji Doya, John O'Doherty, Masamichi Sakagami

Learning about Multiple Attributes of Reward in Pavlovian Conditioning
ANDREW R DELAMATER, STEPHEN OAKESHOTT

Should I Stay or Should I Go?. Transformation of Time-Discounted Rewards in Orbitofrontal Cortex and Associated Brain Circuits
MATTHEW R ROESCH, DONNA J CALU, KATHRYN A BURKE, GEOFFREY SCHOENBAUM

Model-Based fMRI and Its Application to Reward Learning and Decision Making
JOHN P O'DOHERTY, ALAN HAMPTON, HACKJIN KIM

Splitting the Difference. How Does the Brain Code Reward Episodes?
BRIAN KNUTSON, G. ELLIOTT WIMMER

Reward-Related Responses in the Human Striatum
MAURICIO R DELGADO

Integration of Cognitive and Motivational Information in the Primate Lateral Prefrontal Cortex
MASAMICHI SAKAGAMI, MASATAKA WATANABE

Mechanisms of Reinforcement Learning and Decision Making in the Primate Dorsolateral Prefrontal Cortex
DAEYEOL LEE, HYOJUNG SEO


Resisting the Power of Temptations. The Right Prefrontal Cortex and Self-Control
DARIA KNOCH, ERNST FEHR

Adding Prediction Risk to the Theory of Reward Learning
KERSTIN PREUSCHOFF, PETER BOSSAERTS

Still at the Choice-Point. Action Selection and Initiation in Instrumental Conditioning
BERNARD W BALLEINE, SEAN B OSTLUND

Plastic Corticostriatal Circuits for Action Learning. What's Dopamine Got to Do with It?
RUI M COSTA

Striatal Contributions to Reward and Decision Making. Making Sense of Regional Variations in a Reiterated Processing Matrix
JEFFERY R WICKENS, CHRISTOPHER S BUDD, BRIAN I HYLAND, GORDON W ARBUTHNOTT

Multiple Representations of Belief States and Action Values in Corticobasal Ganglia Loops
KAZUYUKI SAMEJIMA, KENJI DOYA

Basal Ganglia Mechanisms of Reward-Oriented Eye Movement
OKIHIDE HIKOSAKA

Contextual Control of Choice Performance. Behavioral, Neurobiological, and Neurochemical Influences
JOSEPHINE E HADDON, SIMON KILLCROSS

A "Good Parent" Function of Dopamine. Transient Modulation of Learning and Performance during Early Stages of Training
JON C HORVITZ, WON YUNG CHOI, CECILE MORVAN, YANIV EYNY, PETER D BALSAM

Serotonin and the Evaluation of Future Rewards. Theory, Experiments, and Possible Neural Mechanisms
NICOLAS SCHWEIGHOFER, SAORI C TANAKA, KENJI DOYA

Receptor Theory and Biological Constraints on Value
GREGORY S BERNS, C. MONICA CAPRA, CHARLES NOUSSAIR

Reward Prediction Error Computation in the Pedunculopontine Tegmental Nucleus Neurons
YASUSHI KOBAYASHI, KEN-ICHI OKADA

A Computational Model of Craving and Obsession
A. DAVID REDISH, ADAM JOHNSON

Calculating the Cost of Acting in Frontal Cortex
MARK E WALTON, PETER H RUDEBECK, DAVID M BANNERMAN, MATTHEW F. S RUSHWORTH

Cost, Benefit, Tonic, Phasic. What Do Response Rates Tell Us about Dopamine and Motivation?
YAEL NIV

More than Trust: Oxytocin Increases Generosity

It was known since a couple of years that oxytocin (OT) increases trust (Kosfeld, et al., 2005): in the Trust game, players transfered more money once they inhale OT. Now recent research also suggest that it increases generosity. In a paper presented at the ESA (Economic Science Association, an empirically-oriented economics society) meeting, Stanton, Ahmadi, and Zak, (from the Center for Neuroeconomics studies) showed that Ultimatum players in the OT group offered more money (21% more) than in the placebo group--$4.86 (OT) vs. $4.03 (placebo).
They defined generosity as "an offer that exceeds the average of the MinAccept" (p.9), i.e., the minimum acceptable offer by the "responder" in the Ultimatum. In this case, offers over $2.97 were categorized as generous. Again, OT subjects displayed more generosity: the OT group offered $1.86 (80% more) over the minimum acceptable offer, while placebo subjects offered $1.03.

Interestingly, OT subjects did not turn into pure altruist: they make offers (mean $3.77) in the Dictator game similar to placebo subjects (mean $3.58, no significant difference). Thus the motive is neither direct nor indirect reciprocity (Ultimatum were blinded one-shot so there is no tit-for-tat or reputation involved here). It is not pure altruism, according to Stanton et al., (or "strong reciprocity"--see this post on the distinction between types of reciprocity) because the threat of the MinAccept compels players to make fair offers. They conclude that generosity in enhanced because OT affects empathy. Subjects simulate the perspective of the other player in the Ultimatum, but not in the Dictator. Hence, generosity "runs" on empathy: in empathizing context (Ultimatum) subjects are more generous, but in non-empathizing context they don't--in the dictator, it is not necessary to know the opponent's strategy in order to compute the optimal move, since her actions has no impact on the proposer's behavior. It would be interesting to see if there is a different OT effect in basic vs. reenactive empathy (sensorimotor vs. deliberative empathy; see this post).

Interested readers should also read Neural Substrates of Decision-Making in Economic Games, by one of the author of the study (Stanton): in her PhD Thesis, she desribes many neurpeconomic experiences.

[Anecdote: I once asked people of the ESA why they call their society like that: all presented papers were experimental, so I thought that the name should reflect the empirical nature of the conference. They replied judiscioulsy : "Because we think that it's how economics should be done"...]

References

• Kosfeld, M., Heinrichs, M., Zak, P. J., Fischbacher, U., & Fehr, E. (2005). Oxytocin increases trust in humans. Nature, 435(7042), 673-676.
• Stanton, A. A. (2007): Neural Substrates of Decision-Making in Economic Games. Published in: Journal of Dissertation Issue 1.Volume 1 (2007): pp. 1-63.
• Stanton, A. A., Ahmadi, S., & Zak, P. J. (2007). Oxytocin Increases Generosity. Paper presented at the Economic Science Association 2007 World Meeting.

Ten major ideas and findings in behavioral decision research in the last 50 years

1. judgment can be modeled
2. bounded rationality
3. to understand decision making, understanding tasks is more important than understanding people
4. levels of aspiration or reference points and loss aversion
5. heuristic rules
6. adding and the importance of simple models
7. the search for confirmation
8. the evasive nature of risk perception
9. the construction of preference
10. the roles of emotions, affect, and intuition.

from:

• Hogarth, R. (2007). Behavioral Decision Making at 50: Achievements, Prospects, and Challenges. In Developments on Experimental Economics (pp. 35-58).

The Top 10 Most Important Papers in Neuroeconomics

The choice wasn't easy, and I may be influenced by my research interests, but here is what I think are the most important papers in the field:

• Camerer, C., Loewenstein, G., & Prelec, D. (2005). Neuroeconomics: How neuroscience can inform economics. Journal of Economic Literature, 43(1), 9-64.

Written by famous behavioral economists, this extensive review paper suggests how economics can be theoretically and empirically informed by neuroeconomics.

• Glimcher, P. W., & Rustichini, A. (2004). Neuroeconomics: The Consilience of Brain and Decision. Science, 306(5695), 447-452.

An analysis of the theoretical relationship between biology, economics, neuroscience and psychology.

• Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The neural basis of economic decision-making in the Ultimatum Game. Science, 300(5626), 1755-1758.

Famous paper showing that unfair offers elicit activity in the anterior insula, an area associated with disgust (but not when they interact with a computer).

• Zak, P. J. (2004). Neuroeconomics. Philos Trans R Soc Lond B Biol Sci, 359(1451), 1737-1748.

A review paper that provides a complete introduction to neuroscience (methods, brain functions, etc.) and neuroeconomics.

• Kosfeld, M., Heinrichs, M., Zak, P. J., Fischbacher, U., & Fehr, E. (2005). Oxytocin increases trust in humans. Nature, 435(7042), 673-676.

Subjects who received oxytocin via nasal spray are more trusting.

• Rilling, J., Gutman, D., Zeh, T., Pagnoni, G., Berns, G., & Kilts, C. (2002). A neural basis for social cooperation. Neuron, 35(2), 395-405.

Players who initiate and players who experiment mutual cooperation display activation in nucleus accumbens and other reward-related areas.

• de Quervain, D. J., Fischbacher, U., Treyer, V., Schellhammer, M., Schnyder, U., Buck, A., et al. (2004). The neural basis of altruistic punishment. Science, 305(5688), 1254-1258.

Punishing cheaters, in the trust game, activates the nucleus accumbens, a subcortical structure involved in pleasure.

• Montague, P. R., & Berns, G. S. (2002). Neural Economics and the Biological Substrates of Valuation. Neuron, 36(2), 265-284.

One of the first application of utility theory to dopaminergic systems.

• McCabe, K., Houser, D., Ryan, L., Smith, V., & Trouard, T. (2001). A functional imaging study of cooperation in two-person reciprocal exchange. PNAS, 98(20), 11832-11835.

The first imaging study in game theory. Decision-makers are more likely to cooperate with real humans than with computers and cooperators have a significantly different brain activation in the two conditions.

• Platt, M. L., & Glimcher, P. W. (1999). Neural correlates of decision variables in parietal cortex. Nature, 400(6741), 238.

The first genuine neuroeconomics paper. Lateral intraparietal area (LIP) activity predicts visual-saccadic decision-making, encode the desirabilities of making particular movements.

My critera are citations, influence, historical/theoretical importance and relevance for understanding decision-making

Altruism: a research program

Phoebe: I just found a selfless good deed; I went to the park and let a bee sting me.
Joey: How is that a good deed?
Phoebe: Because now the bee gets to look tough in front of his bee friends. The bee is happy and I am not.
Joey: Now you know the bee probably died when he stung you?
Phoebe: Dammit!
- [From Friends, episode 101]

Altruism is a lively research topic. The evolutionary foundations, neural substrates, psychological mechanisms, behavioral manifestations, formal modeling and philosophical analyses of cooperation constitute a coherent—although not unified—field of inquiry. See for instance how neuroscience, game theory, economic, philosophy, psychology and evolutionary theory interact in Penner et al. 2005; Hauser 2006; Fehr and Fischbacher 2002; Fehr and Fischbacher 2003. The nature of prosocial behavior, from kin selection to animal cooperation to human morality can be considered as a progressive Lakatosian research programs. Altruism has a great conceptual "sex-appeal" because it is mystery for two types of theoreticians: biologists and economists. They both wonder why an animal or an economic agent would help another: since these agents maximize fitness/utility, altruistic behavior is suboptimal. Altruims (help, trust, fairness, etc.) seems intuitively incoherent with economic rationality and biological adaptation, with markets and natural selection. Or is it?

In the 60's, biologists challenged the idea that natural selection is incompatible with altruism. Hamilton (1964a, 1964b) and Trivers (1971) showed that biological altruism makes sense. An animal X might behave altruistically toward another Y because they are genetically related: in doing so, X maximize the copying of its gene, since many of its genes will be hosted in Y. Thus the more X and Y are genetically related, the more X will be ready to help Y. This is kin altruism. Altruism can also be reciprocal: scratch my back and I'll scratch yours. Tit-for-tat, or reciprocal altruism also makes sense because by being altruistic, one may augments its payoff. X helps Y, but the next time Y will help X; thus it is better to help than not to help. In both cases, the idea is that altruism is a mean not an end. Others argue that more complex types of altruisms exists. For instance, X can help Y because Y already helped Z (indirect reciprocity). In this case, the tit-for-tat logic is extended to agents that the helper did not meet in the past. Generalized reciprocity (see this previous post) is another type of altruism: helping someone because someone helped you in the past. This altruism does not require memory or personal identification. X helps someone because someone else helped X. Finally, Strong reciprocity is the idea that humans display genuine altruism: strong reciprocators cooperate with cooperators, do not cooperate with cheaters, and are ready to punish cheaters even at a cost to themselves. Their proponents argue that it evolved through group selection.

Experimental economics and neuroeconomics also challenged the idea of rational, greedy, selfish actor (the Ayn Rand hero). Experimental game theory showed that, contrarily to orthodox game theory, subjects cooperate massively in prisoner’s dilemma (Ledyard, 1995; Sally, 1995). Rilling et al. showed that players enjoy cooperating. Players who initiate and players who experience mutual cooperation display activation in nucleus accumbens and other reward-related areas such as the caudate nucleus, ventromedial frontal/orbitofrontal cortex, and rostral anterior cingulate cortex (Rilling et al., 2002). In another experiment, the presentation of faces of intentional cooperators caused increased activity in reward-related areas (Singer et al. 2004). In the ultimatum game, proposers make ‘fair’ offers, about 50% of the amount, responders tend to accept these offers and reject most of the ‘unfair’ offers (less than 20%;Oosterbeek et al., 2004). Brain scans of people playing the ultimatum game indicate that unfair offers trigger, in the responders’ brain, a ‘moral disgust’: the anterior insula (associated with negative emotional states like disgust or anger) is more active when unfair offers are proposed (Sanfey, Rilling, Aronson, Nystrom, & Cohen, 2003). Subjects experiment this affective reaction to unfairness only when the proposer is a human being: the activation is significantly lower when the proposer is a computer. Moreover, the anterior insula activation is proportional to the degree of unfairness and correlated with the decision to reject unfair offers (Sanfey et al., 2003: 1756). Fehr and Fischbacher (2002) suggested that economic agents are inequity-averse and have prosocial preferences. Thus they modified the utility functions to account for behavioral (and now neural) data. In Moral Markets: The Critical Role of Values in the Economy, Paul Zak proposes a radically different conception of morality in economics:

The research reported in this book revealed that most economic exchange, whether with a stranger or a known individual, relies on character values such as honesty, trust, reliability, and fairness. Such values, we argue, arise in the normal course of human interactions, without overt enforcement—lawyers, judges or the
police are present in a paucity of economic transactions (...). Markets are moral in two senses. Moral behavior is necessary for exchange in moderately regulated markets, for example, to reduce cheating without exorbitant transactions costs. In addition, market exchange itself can lead to an understanding of fair-play that can build social capital in nonmarket settings. (Zak, forthcoming)

See how this claim is similar to :

The two fundamental principles of evolution are mutation and natural selection. But evolution is constructive because of cooperation. New levels of organization evolve when the competing units on the lower level begin to cooperate. Cooperation allows specialization and thereby promotes biological diversity. Cooperation is the secret behind the open-endedness of the evolutionary process. Perhaps the most remarkable aspect of evolution is its ability to generate cooperation in a competitive world. Thus, we might add "natural cooperation" as a third fundamental principle of evolution beside mutation and natural selection. (Nowak, 2006)

Hence, biological and economic theorizing followed a similar path: they started first with the assumption that agents value only their own payoff; evidence suggested then that agents behave altruistically and, finally, theoretical models were amended and now incorporate different kinds of reciprocity.

So is it good news? Are we genuinely altruistic? First a precision: there is a difference between biological and psychological altruism, and the former does not entail the latter; biological altruism is about fitness consequencences (survival and reproduction), while psychological altruism is about motivation and intentions:

Where human behaviour is concerned, the distinction between biological altruism, defined in terms of fitness consequences, and ‘real’ altruism, defined in terms of the agent's conscious intentions to help others, does make sense. (Sometimes the label ‘psychological altruism’ is used instead of ‘real’ altruism.) What is the relationship between these two concepts? They appear to be independent in both directions (...). An action performed with the conscious intention of helping another human being may not affect their biological fitness at all, so would not count as altruistic in the biological sense. Conversely, an action undertaken for purely self-interested reasons, i.e. without the conscious intention of helping another, may boost their biological fitness tremendously (Biological Altruism, Stanford Encyclopedia of Philosophy; see also a forthcoming paper by Stephen Stich and the classic Sober & Wilson 1998).

The interesting question, for many researchers, is then: what is the link between biological and psychological altruism? A common view suggests non-human animals are biological altruists, while humans are also psychological atruists. I would like argue against this sharp divide and briefly suggest three things:

1. Non-humans also display psychological altruism
2. Human altruism is strongly influenced by biological motives
3. Prosocial behavior in human and non-human animals should be understood as a single phenomena: cooperation in the economy of nature

1. Non-humans also display psychological altruism


A discussed in a previous post, a recent research paper showed that rats exhibit generalized reciprocity: rats who had previously been helped were more likely (20%) to help unknown partner than rats who had not been helped. Although the authors of the paper take a more prudent stance, I consider generalized reciprocity as psychological altruism (remember, it can be both): rats cooperate because they "feel good", and that feeling is induced by cooperation, not by a particular agent. Hence their brain value cooperation (probably thanks to hormonal mechanisms similar to ours) in itself, even if there is no direct tit-for-tat. In the same edition of PLoS biology, primatologist Frans de Waal (2007) also argue that animals show signs of psychological altruism; it it particularly clear in an experiment (Warneken et al, again, in the same journal) that show that chimpanzees are ready to help unknown humans and conspecifics (hence ruling out kin and tit-for-tat altruism), even at a cost to themselves. Here is the description of the experiments:

In the first experiment, the chimpanzee saw a person unsuccessfully reach through the bars for a stick on the other side, too far away for the person, but within reach of the ape. The chimpanzees spontaneously helped the reaching person regardless of whether this yielded a reward, or not. A similar experiment with 18-month-old children gave exactly the same outcome. Obviously, both apes and young children are willing to help, especially when they see someone struggling to reach a goal. The second experiment increased the cost of helping. The chimpanzees were still willing to help, however, even though now they had to climb up a couple of meters, and the children still helped even after obstacles had been put in their way. Rewards had been eliminated altogether this time, but this hardly seemed to matter. One could, of course, argue that chimpanzees living in a sanctuary help humans because they depend on them for food and shelter. How familiar they are with the person in question may be secondary if they simply have learned to be nice to the bipedal species that takes care of them. The third and final experiment therefore tested the apes' willingness to help each other, which, from an evolutionary perspective, is also the only situation that matters. The set-up was slightly more complex. One chimpanzee, the Observer, would watch another, its Partner, try to enter a closed room with food. The only way for the Partner to enter this room would be if a chain blocking the door were removed. This chain was beyond the Partner's control—only the Observer could untie it. Admittedly, the outcome of this particular experiment surprised even me—and I am probably the biggest believer in primate empathy and altruism. I would not have been sure what to predict given that all of the food would go to the Partner, thus creating potential envy in the Observer. Yet, the results were unequivocal: Observers removed the peg holding the chain, thus yielding their Partner access to the room with food (de Waal)

(image from Warneken et al video)

2. Human altruism is strongly influenced by biological motives

In many cases, human altruism appear as a complex version of biological altruism (see Burnham & Johnson, 2005. The Biological and Evolutionary Logic of Human Cooperation for a review). For instance, Madsen et al. (2007) showed that humans behave more altruistically toward their own kin when there is a significant genuine cost (such as muscular 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. Other studies showed that facial similarity enhances trust (DeBruine 2002). In each cases, there is a mechanism whose function is to negotiate personal investments in relationships in order to promote the copying of genes housed either in people of—or people who seems to be of—our kin.

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, Katok, and Zwick 1998). Offers are even lower in the dictator game when donation is fully anonymous (Hoffman et al. 1994). 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 and 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, Nettle, and Roberts 2006). The sole fact of speaking of ghosts enchances honest behavior in a competitive taks (Bering, McLeod, and Shackelford 2005), while priming subjects with the God concept in the anonymous dictator game (Shariff and Norenzayan in press).

These reflections also applies to altruistic punishment. First, it is enhanced by an audience. (Kurzban, DeScioli, and O'Brien 2007) showed that with a dozen participants, punishment expenditure tripled. Again, appareant altruism is instrumental in personal satisfaction. Other research suggest that altruism is also an advantage in sexual selection: "people preferentially direct cooperative behavior towards more attractive members of the opposite sex. Furthermore, cooperative behavior increases the perceived attractiveness of the cooperator" (Farrelly et al., 2007).

An interesting framework to understand altruims is Hardy (no relation with me) & Van Vugt (2006) theory of competitive altruism: "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." We need, however, a more general perspective.


3. Prosocial behavior in human and non-human animals should be understood as a single phenomena: cooperation in the economy of nature

All organic beings are striving to seize on each place in the economy of nature - (Darwin, [1859] 2003, p. 90)

With Darwin, natural economy began to be understood with the conceptual tools of political economy. The division of labor, competition (“struggle” in Darwin’s words), trading, cost, the accumulation of innovations, the emergence of complex order from unintentional individual actions, the scarcity of resources and the geometric growth of populations are ideas borrowed from Adam Smith, Thomas Malthus, David Hume and other founders of modern economics. Thus, the economy of nature ceased to be an abstract representation of the universe and became a depiction of the complex web of interactions between biological individuals, species and their environment—the subject matter of ecology. Consequently, Darwin’s main contributions are his transforming biology into a historical science—like geology—and into an economic science.

I take the economy-of-nature principle to be a refinement of the natural selection principle: while it describes general features of the biosphere, it puts emphasis on the intersection between individual biographies and natural selection, and especially on decision-making. On the one hand, the decisions biological individuals make increase or decrease their fitness, and thus good decision-makers are more likely to propagate their genes. On the other hand, natural selection is likely to favor good decision-makers and to get rid of bad decision-makers. Thus, if our best descriptive theories of animal and human economic behavior indicate that all these agents have prosocial preferences and make altruistic decisions, then these preferences and decisions are not maladaptive and irrational. They must have an evolutionary and an economic payoff. Markets and natural selections requires cooperation, even if the deep motivations are partly selfish. Fairness, equity and honesty are social goods in the economy of nature, human and non-human.

• Bateson, M., D. Nettle, and G. Roberts. 2006. Cues of being watched enhance cooperation in a real-world setting. Biology Letters 12:412-414.
• Bering, J. M., K. McLeod, and T. K. Shackelford. 2005. Reasoning about Dead Agents Reveals Possible Adaptive Trends. Human Nature 16 (4):360-381.
• Bolton, G. E., E. Katok, and R. Zwick. 1998. Dictator Game Giving: Rules of Fairness versus Acts of Kindness International Journal of Game Theory 27 269-299
• Burnham, T. C., and D. D. P. Johnson. 2005. The Biological and Evolutionary Logic of Human Cooperation. Analyse & Kritik 27:113-135.
• DeBruine, L. M. 2002. Facial resemblance enhances trust. Proc Biol Sci 269 (1498):1307-12.
• de Waal FBM (2007) With a Little Help from a Friend. PLoS Biol 5(7): e190 doi:10.1371/journal.pbio.0050190
• Farrelly, D., J. Lazarus, and G. Roberts. 2007. Altruists attract. Evolutionary Psychology 5 (2):313-329.
• Fehr, E., and U. Fischbacher. 2002. Why social preferences matter: The impact of non-selfish motives on competition, cooperation and incentives. Economic Journal 112:C1-C33.
• Fehr, Ernst, and Urs Fischbacher. 2003. The nature of human altruism. Nature 425 (6960):785-791.
• Hamilton, W. D. 1964a. The genetical evolution of social behaviour. I. J Theor Biol 7 (1):1-16.
• ———. 1964b. The genetical evolution of social behaviour. II. J Theor Biol 7 (1):17-52.
• Hauser, Marc D. 2006. Moral minds : how nature designed our universal sense of right and wrong. New York: Ecco.
• Ledyard, J. O. 1995. Public goods: A survey of experimental research. In Handbook of experimental economics, edited by J. H. Kagel and A. E. Roth: Princeton University Press.
• Haley, K., and D. Fessler. 2005. Nobody’s watching? Subtle cues affect generosity in an anonymous economic game. Evolution and Human Behavior 26 (3):245-56.
• Hoffman, E., K. Mc Cabe, K. Shachat, and V. Smith. 1994. Preferences, Property Rights, and Anonymity in Bargaining Experiments. Games and Economic Behavior 7:346–380.
• Kurzban, Robert, Peter DeScioli, and Erin O'Brien. 2007. Audience effects on moralistic punishment. Evolution and Human Behavior 28 (2):75-84.
• Madsen, Elainie A., Richard J. Tunney, George Fieldman, Henry C. Plotkin, Robin I. M. Dunbar, Jean-Marie Richardson, and David McFarland. 2007. Kinship and altruism: A cross-cultural experimental study. British Journal of Psychology 98:339-359.
• Penner, Louis A., John F. Dovidio, Jane A. Piliavin, and David A. Schroeder. 2005. Prosocial behavior: Multilevel Perspectives. Annual Review of Psychology 56 (1):365-392.
• Okasha, Samir, "Biological Altruism", The Stanford Encyclopedia of Philosophy (Summer 2005 Edition), Edward N. Zalta (ed.), URL = http://plato.stanford.edu/archives/sum2005/entries/altruism-biological/.
• Stewart-Williams, Steve. 2007. Altruism among kin vs. nonkin: effects of cost of help and reciprocal exchange. Evolution and Human Behavior 28 (3):193-198.
• Nowak, M. A. 2006. Five Rules for the Evolution of Cooperation. Science 314 (5805):1560-1563.
• Oosterbeek, H., Randolph S., and G. van de Kuilen. 2004. Differences in Ultimatum Game Experiments: Evidence from a Meta-Analysis. Experimental Economics 7:171-188.
• Rilling, J., D. Gutman, T. Zeh, G. Pagnoni, G. Berns, and C. Kilts. 2002. A neural basis for social cooperation. Neuron 35 (2):395-405.
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• Sally, D. 1995. Conversations and cooperation in social dilemmas: a meta-analysis of experiments from 1958 to 1992. Rationality and Society 7:58 – 92
• Sanfey, A. G., J. K. Rilling, J. A. Aronson, L. E. Nystrom, and J. D. Cohen. 2003. The neural basis of economic decision-making in the Ultimatum Game. Science 300 (5626):1755-8.
• Shariff, A.F. , and A. Norenzayan. in press. God is watching you: Supernatural agent concepts increase prosocial behavior in an anonymous economic game. Psychological Science.
• Singer, T., S. J. Kiebel, J. S. Winston, R. J. Dolan, and C. D. Frith. 2004. Brain responses to the acquired moral status of faces. Neuron 41 (4):653-62.
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• Zak, P. J., ed. forthcoming. Moral Markets: The Critical Role of Values in the Economy. Princeton, N.J.: Princeton University Press.

Decision-Making: A Neuroeconomic Perspective

I put a new paper on my homepage :

Decision-Making: A Neuroeconomic Perspective

Here is the abstract:

This article introduces and discusses from a philosophical point of view the nascent field of neuroeconomics, which is the study of neural mechanisms involved in decision-making and their economic significance. Following a survey of the ways in which decision-making is usually construed in philosophy, economics and psychology, I review many important findings in neuroeconomics to show that they suggest a revised picture of decision-making and ourselves as choosing agents. Finally, I outline a neuroeconomic account of irrationality.

Hardy-Vallée, B. (forthcoming). Decision-making: a neuroeconomic perspective. Philosophy Compass. [PDF]

This paper is the first in my philosophical exploration of neuroeconomics, and I would gladly welcome your comments and suggestions for subsequent research. Email me at benoithv@gmail.com.

Decision-Making in Philosophy, Economics and Psychology

(An overview of different conceptions of decision-making in philosophy, economics and psychology.)

Rational agents display their rationality mainly in making decisions. Certain decisions are more basic (turn left or turn right), others are crucial issues (“to be or not to be”). In any case, being an agent entails making choices. Even abstinence is decision, as thinkers like William James or Jean-Paul Sartre once pointed out. In our ordinary use of the word, our folk-psychology inclines us to believe that making a decision implies a deliberation: a weighting of beliefs, desires and intentions (Malle et al., 2001). In philosophy of mind, the standard conception of decision-making equates deciding and forming an intention before an action (Davidson, 1980, 2004; Hall, 1978; Searle, 2001). According to different analysis, this intention can be equivalent to, inferred from or accompanied by, desires and beliefs. Thus, the decisions rational agents make are motivated by reasons. Rational actions are explained by these reasons, the purported causes of the actions. Beliefs and desires are also constitutive of rationality because they justify rational action: there is a logical coherence between beliefs, desires and actions. Actions are irrational when their causes do not justify them. Beliefs and desires are embedded in our interpretations of rational agents as rational agents: “[a]nyone who superimposes the longitudes of desire and the latitudes of belief is already attributing rationality” (Sorensen, 2004, p. 291). Hence, on this account, X is a rational agent if X can be interpreted as an agent whose actions are justified by the beliefs and desires that caused her to make a particular choice. The attribution of rational agency is then based on the success of applying an interpretation scheme that presuppose the rationality of the agent, such as the Dennettian "intentional stance", the Davidsonian "principle of charity" or the Popperian "principle of rationality" (Davidson, 1980; Dennett, 1987; Popper, 1994).
The abstract structure of this interpretation scheme has been formalized by theoretical economics and rational-choice theory. Economics, according to a standard definition by Lionel Robbins, is the “science which studies human behavior as a relationship between ends and scarce means which have alternative uses” (Robbins, 1932, p. 15). This definition shows the centrality of decision-making in economic science: since means are scarce, behavior should use them efficiently. The two branches of rational-choice theory, decision theory and game theory, specifies the formal constraints on optimal decision-making in individual and interactive contexts. An individual agent facing a choice between two actions can make a rational decision is she takes into account two parameters: the probability and utility of the consequences of each action. By multiplying the subjective probability by the subjective utility of an action’s outcomes, she can select the action that have the higher subjective expected utility(see Baron, 2000, for an introduction). Game theory models agents making decisions in a strategic context, where the preferences of at least another agent must be taken into account. Decision-making is represented as the selection of a strategy in a game, that is, a set of rules that dictates the range of possible actions and the payoffs of any conjunct of actions. Thus, economic decision-making is mainly about computing probabilities and utilities (Weirich, 2004 ). The philosopher’s beliefs-desire model is hence reflected in the economist’s probability-utility model: probabilities represent beliefs while utilities represent desires.
Rational-choice theory can be construed as a normative theory (what agents should do) or as a descriptive one (what agents do). On its descriptive construal, rational-choice theory is a framework for building predictive models of choice behavior: which lottery an agent would select, whether an agent would cooperate or not in a prisoner’s dilemma, etc. Experimental economics, behavioral economics, cognitive science and psychology (I will refer to these empirical approaches of rationality as ‘psychology’) use this model to study how subjects make decisions and which mechanisms they rely on for choosing. These patterns of inference and behavior can then be compared with rational-choice theory. In numerous studies, Amos Tversky and Daniel Kahneman showed that decision-makers’ judgments deviate markedly from normative theories (Kahneman, 2003; Kahneman et al., 1982; Tversky, 1975). Subjects tend to make decisions according to their ‘framing’ of a situation (the way they represent the situation, e.g. as a gain or as a loss), and exhibit loss-, risk- and ambiguity-aversion (Camerer, 2000; Kahneman & Tversky, 1979, 1991, 2000; Thaler, 1980). In most of their experiments, Tversky and Kahneman asked subjects to choose among different options in fictive situations in order to assess the similarity between natural ways of thinking and normative decision theory. For instance, subjects were presented the following situation (Tversky & Kahneman, 1981):

Imagine that the United States is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs to combat the disease have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows:
- If Program A is adopted, 200 people will be saved
- If Program B is adopted, there is a one-third probability that 600 people will be saved and a two-thirds probability that no people will be saved.
Which of the two programs would you favor?

Most of the respondent opted for A, the risk-averse solution. When respondent were offered the following version:
- If Program A is adopted, 400 people will die
- If Program B is adopted, there is a one-third probability that nobody will die and a two-thirds probability that 600 people will die

Although Program A has exactly the same outcome in both versions (400 people die, 200 will be saved), in the second version Program B is the most popular. Thus, not only are subjects risk-averse, but their risk-aversion depends on the framing of the situation. Subjects have a different attitude whether a situation is presented as a gain or as a loss. The study of decision-making is thus the study of the heuristics and biases that impinge upon human judgment. The explanatory target is the discrepancies between rational-choice theory and human psychology. Just like the psychology of perception tries to explain visual illusions (e.g. the Muller-Lyer illusion), the psychology of decision tries to explain cognitive illusions: why agents prefer systematically one kind of prospect to another when rational-choice theory recommends another. Loss-aversion, for instance, can be explained by the shape of the value function: it is concave for gains and convex for losses. Thus loosing $100 hurts more than winning $100 makes one happy.
Proponent of the ecological rationality approach suggested nonetheless that these heuristics and bias might be adaptive in certain contexts and that failures of human rationality can be lessen in proper ecological conditions. For instance, when probabilities are presented as frequencies (6 out of 10) instead of subjective probabilities (60%), results tend to be much better, partly because we encounter more sequences of events than degrees of beliefs. These heuristics might be ‘fast and frugal’ procedures tailored for certain tasks, thus leading to suboptimal outcomes in other contexts. (Gigerenzer, 1991; Gigerenzer et al., 1999). Or they could be vestigial adaptations to ecological and social environments where our hunters-gatherers ancestors lived. Thus heuristics may not completely ineffective.


References

Baron, J. (2000). Thinking and deciding (3rd ed.). Cambridge, UK ; New York: Cambridge University Press.
Camerer, C. (2000). Prospect theory in the wild. In D. Kahneman & A. Tversky (Eds.), Choice, values, and frames (pp. 288-300). New York: Cambridge University Press.
Davidson, D. (1980). Essays on actions and events. Oxford: Oxford University Press.
Davidson, D. (2004). Problems of rationality. Oxford: Oxford University Press.
Dennett, D. C. (1987). The intentional stance. Cambridge, Mass.: MIT Press.
Gigerenzer, G. (1991). How to make cognitive illusions disappear: Beyond heuristics and biases. European Review of Social Psychology, 2(S 83), 115.
Gigerenzer, G., Todd, P. M., & ABC Research Group. (1999). Simple heuristics that make us smart. New York: Oxford University Press.
Hall, J. W. (1978). Deciding as a way of intending. The Journal of Philosophy, 75(10), 553-564.
Kahneman, D. (2003). A perspective on judgment and choice: Mapping bounded rationality. Am Psychol, 58(9), 697-720.
Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty : Heuristics and biases. Cambridge ; New York: Cambridge University Press.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263-291.
Kahneman, D., & Tversky, A. (1991). Loss aversion in riskless choice: A reference-dependent model. The Quartely Journal of Economics, 106(4), 1039-1061.
Kahneman, D., & Tversky, A. (2000). Choices, values, and frames. Cambridge, UK: Cambridge University Press.
Malle, B. F., Moses, L. J., & Baldwin, D. A. (2001). Intentions and intentionality : Foundations of social cognition. Cambridge, Mass.: MIT Press.
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 (pp. 154-184). London: Routledge.
Robbins, L. (1932). An essay on the nature and signifiance of economic science. London Macmillan.
Searle, J. (2001). Rationality in action. Cambridge, Mass.: MIT Press.
Sorensen, R. (2004). Charity implies meta-charity. Philosophy and Phenomenological Research, 26, 290-315.
Thaler, R. H. (1980). Toward a positive theory of consumer choice. Journal of Economic Behavior & Organization, 1(1), 39-60.
Tversky, A. (1975). A critique of expected utility theory: Descriptive and normative considerations. Erkenntnis, V9(2), 163-173.
Tversky, A., & Kahneman, D. (1981). The framing of decisions and psychology of choice. Science, 211, 453-458.
Weirich, P. (2004 ). Economic rationality. In A. in Mele, & Rawlings, P. (Ed.), Oxford handbook of rationality (pp. 380–398). Oxford: Oxford University Press.

Equality preference and inequality aversions

In a letter to Nature , a group of political scientist and anthropologist report an experiment designed to test equality preference and inequality aversions. the design was simple:

Subjects are divided into groups having four anonymous members each. Each player receives a sum of money randomly generated by a computer. Subjects are shown the payoffs of other group members for that round and are then provided an opportunity to give 'negative' or 'positive' tokens to other players. Each negative token reduces the purchaser's payoff by one monetary unit (MU) and decreases the payoff of a targeted individual by three MUs; positive tokens decrease the purchaser's payoff by one monetary unit (MU) and increase the targeted individual's payoff by three MUs. Groups are randomized after each round to prevent reputation from influencing decisions; interactions between players are strictly anonymous and subjects know this. Also, by allowing participants more than one behavioural alternative, the experiment eliminates possible experimenter demand effects—if subjects were only permitted to punish, they might engage in this behaviour because they believe it is what the experimenters want.

The results support what is often referred to as the "Robin Hood effect": richer individuals were heavily penalized, while poorer received more gift. This would support the hypothesis of Strong Reciprocity (SR), put forth by Fehr, Camerer, Gintins, and many other scholar in behavioral economics. SR implies that individuals will cooperate with cooperator (reciprocal altruism), will not cooperate with cheaters, and are even ready to punish those who cheat others (altruistic punishment):

“people tend to behave prosocially and punish antisocial behavior at cost to themselves, even when the probability of future interactions is low or zero. We call this strong reciprocity." (Gintis, H. (2000). Strong reciprocity and human sociality. Journal of Theoretical Biology, 206(2), p. 177)

And of course, SR implies that individual will be inequity-averse. SR proponent go further, and state that we an innate propensity for altruistic punishment. That’s all well and good, but why so much moral optimism? Couldn't it be that we are selfish agents and that our mechanisms of decision-making aims primarily at maximizing positive outcomes and minimizing negative ones? We feel good when we punish bad guys, we feel bad when someone make unfair offers to us in the ultimatum game (neuroeconomics studies showed that). I agree with SRers that we are not cold logical egoists, but would favor another approach I call "Hot Logic": (from an abstract of a forthcoming talk):

human agents are selfish agents adapted to trade, exchange and partner selection in biological markets (Noë et al., 2001). Cognitive mechanisms of decision-making aims primarily at maximizing positive outcomes and minimizing negative ones. This initial hedonism is gradually modulated by social norms, by which agents learn how to maximise their utility given the norms. The ‘hot logic’ approach provide a simpler explanation of cooperation and fairness: subjects make ‘fair’ offers in the ultimatum game because they know their offer would be rejected otherwise. Responders affective reaction to ‘unfair offers’ is in fact a reaction to the loss of an expected monetary gain: they anticipated that the proposer would comply with social norms. This claim is supported by other imaging studies showing that loss of money can be aversive, and that actual and counterfactual utility recruit the same neural resources (Delgado et al., 2006; Montague et al., 2006). This approach explains why subjects make lower offers in the dictator game (an ultimatum game in which the responder make an offer and the responder's role is entirely passive) than in the ultimatum, why, when using a computer displaying eyespots, almost twice as many participants transfer money in the dictator (Haley & Fessler, 2005), and why attractive people are offered more in the ultimatum (Solnick & Schweitzer, 1999). In every case, agents seek to maximize a complex hedonic utility function, where the reward and the losses can be monetary, emotional or social (reputation, acceptance, etc.). SR is thus seen as cooperative habits that are not repaid (Burnham & Johnson, 2005)