Defining neuroeconomics

Here is a small anthologies of definition of neuroeconomics. I put some of them on the "Neuroeconomics" entry of Citizendium I recently begin to write.

The first definition, the older mention of the term "neuroeconomics", goes back to 1991. It was purely prospective, but it already defined the research agenda:

"Neuroeconomics is here defined as the study of the neural substrates, and associated mental phenomena, of productive and consumptive economic and socioeconomic behavior. Neuroeconomic studies could be carried out in fields such as economics, management science, financial theory, operations research, in the sociologies of work and occupations, and in the study of relationships between social class, class consciousness, and brain function. Other topics that could be extended to a neuroeconomic level of analysis would include rational choice theory, game theory, and the cognitive aspects of economic decision-making."(TenHouten, 1991, pp.390-391)

Zak's definition focuses on neural substrates:

"an emerging transdisciplinary field that uses neuroscientific measurement techniques to identify the neural substrates associated with economic decisions” (Zak, 2004, p. 1737)

Glimcher & Rustichini point to the convergence of disciplines:

“Economics, psychology and neuroscience are converging today in to a single unified discipline with the ultimate aim of providing a single, general theory of human behavior. (…) The goal of this discipline is thus to understand the processes that connect sensation and action by revealing the neurobiological mechanisms by which decisions are made". (Glimcher & Rustichini, 2004, p. 447)

From Neuroeconomist Kevin McCabe:

Neuroeconomics is an interdisciplinary research program with the goal of building a biological model of decision making in economic environments. Neuroeconomists ask, how does the embodied brain enable the mind (or groups of minds) to make economic decisions? By combining techniques from cognitive neuroscience and experimental economics we can now watch neural activity in real time, observe how this activity depends on the economic environment, and test hypotheses about how the emergent mind makes economic decisions. Neuroeconomics allows us to better understand both the wide range of heterogeneity in human behavior, and the role of institutions as ordered extensions of our minds.

My favorite one, from philosopher Don Ross:

“the program for understanding the neural basis of the behavioral response to scarcity” (Ross, 2005, p. 330)

It clearly draws a connection with Lionel Robbin's classical definition of economics as the “science which studies human behavior as a relationship between ends and scarce means which have alternative uses” (Robbins, 1932).

References:

• Glimcher, P. W., & Rustichini, A. (2004). Neuroeconomics: The consilience of brain and decision. Science, 306(5695), 447-452.
• Robbins, L. (1932). An essay on the nature and signifiance of economic science. London Macmillan.
  
• Ross, D. (2005). Economic theory and cognitive science : Microexplanation. Cambridge, Mass.: MIT Press.
• TenHouten, W. D. (1991). Into the wild blue yonder: On the emergence of the ethnoneurologies--the social science-based neurologies and the philosophy-based neurologies. Journal of Social and Biological Systems, 14(4), 381-408.
• Zak, P. J. (2004). Neuroeconomics. Philos Trans R Soc Lond B Biol Sci, 359(1451), 1737-1748.

marginal utility, value and the brain

Economics assumes the principle of diminishing marginal utility, i.e. the utility of a good increases more and more slowly as the quantity consumed increases (Wikipedia). Mathematically, it means that the value of a monetary gain is not a linear function of the monetary value. Before Bernouilli St-Petersburg Paradox (1738]1954), the expected value of a possible gamble was construed as the product of the objective (for instance, monetary) value of its outcomes and its probability. Suppose, then, a gambler is offered the following lottery:

A fair coin is tossed. If the outcome is heads, the lottery ends and you win 2$. If the outcome is tail, toss the coin again. It the outcome is heads, the lottery ends and you win 4$, etc. If the nth outcome is heads, you win 2n.

Summing the products of probability and value leads to an infinite expected value:

(0.5 x 2) + (0.25 x 4) + (0.125 x 8)…. =
1+1+1 …

After 30 tosses, the gambler could win more than 1 billion $. How much would it be worth paying for a ticket? If a rational agent maximizes expected value, he or she must be willing to buy a ticket for this lottery at any finite price, considering that the expected value of this prospect if infinite. But, as Hacking pointed out, “few of us would pay even $25 to enter such a game” (Hacking, 1980). When Bernoulli offered scholars in St-Petersburg to play this lottery, nobody was interested in it. Bernoulli concluded that the utility function is not linear, but logarithmic. Hence the subjective value of 10$ is different, depending whether you are Bill Gates or a homeless. Bernoulli’s discussion of the St-Petersburg paradox is often considered as one of the first economic experiment (Roth, 1993, p. 3).

A new study in neuroeconomics (Tobler et al.) indicates that the brain's valuation mechanisms follow this principle. Subjects in the experiments had to learn whether a particular abstract shape--shown on a computer screen--predicts a monetary reward (a picture of a 20 pence coin) or not (scrambled picture of the coin). If the utility of money has a diminishing marginal value, then money should be more important for poorer people than for richer. "More important" meaning that the former would learn reward prediction partterns faster and would display more activity in reward-related area. Bingo! That's exactly what happened. Midbain dopaminergic regions were more solicited in the poorer. The valuation mechanisms obey diminishing marginal utility.

This suggest that midbain dopaminergic systems (about which I blogged earlier; see also references at the end of this post) are the seat of our natural rationality, or at least one of its major component. These systems compute utility, stimulate motivation and attention, send reward-prediction error signals, learn from these signals and devise behavioral policies. They do not encode anticipated or experienced utility (other zones are recruited for these: the amygdala and nucleus accumbens for experienced utility, the OFC for anticipated utility, etc.), but decision utility, the cost/benefits analysis of a possible decision.


References

• Bernoulli, D. (1738]1954). Exposition of a new theory on the measurement of risk. Econometrica, 22, 23-36.
• Hacking, I. (1980). Strange expectations. Philosophy of Science, 47, 562-567.
• Roth, A. E. (1993). On the early history of experimental economics. Journal of the History of Economic Thought, 15, 184-209.
• Tobler, P. N., Fletcher, P. C., Bullmore, E. T., & Schultz, W. (2007). Learning-related human brain activations reflecting individual finances. Neuron, 54(1), 167-175.

On dopaminergic systems:

• Ahmed, S. H. (2004). Neuroscience. Addiction as compulsive reward prediction. Science, 306(5703), 1901-1902.
• Bayer, H. M., & Glimcher, P. W. (2005). Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47(1), 129.
• Berridge, K. C. (2003). Pleasures of the brain. Brain and Cognition, 52(1), 106.
• Berridge, K. C., & Robinson, T. E. (1998). What is the role of dopamine in reward: Hedonic impact, reward learning, or incentive salience? Brain Res Brain Res Rev, 28(3), 309-369.
• Cohen, J. D., & Blum, K. I. (2002). Reward and decision. Neuron, 36(2), 193-198.
• Daw, N. D., & Doya, K. (2006). The computational neurobiology of learning and reward. Curr Opin Neurobiol, 16(2), 199-204.
• Daw, N. D., & Touretzky, D. S. (2002). Long-term reward prediction in td models of the dopamine system. Neural Comput, 14(11), 2567-2583.
• Dayan, P., & Balleine, B. W. (2002). Reward, motivation, and reinforcement learning. Neuron, 36(2), 285-298.
• Di Chiara, G., & Bassareo, V. (2007). Reward system and addiction: What dopamine does and doesn't do. Curr Opin Pharmacol, 7(1), 69-76.
• Egelman, D. M., Person, C., & Montague, P. R. (1998). A computational role for dopamine delivery in human decision-making. J Cogn Neurosci, 10(5), 623-630.
• Floresco, S. B., & Magyar, O. (2006). Mesocortical dopamine modulation of executive functions: Beyond working memory. Psychopharmacology (Berl), 188(4), 567-585.
• Frank, M. J., Seeberger, L. C., & O'Reilly, R. C. (2004). By carrot or by stick: Cognitive reinforcement learning in parkinsonism. Science, 306(5703), 1940-1943.
• Joel, D., Niv, Y., & Ruppin, E. (2002). Actor-critic models of the basal ganglia: New anatomical and computational perspectives. Neural Netw, 15(4-6), 535-547.
• Kakade, S., & Dayan, P. (2002). Dopamine: Generalization and bonuses. Neural Netw, 15(4-6), 549-559.
• McCoy, A. N., & Platt, M. L. (2004). Expectations and outcomes: Decision-making in the primate brain. J Comp Physiol A Neuroethol Sens Neural Behav Physiol.
• Montague, P. R., Hyman, S. E., & Cohen, J. D. (2004). Computational roles for dopamine in behavioural control. Nature, 431(7010), 760.
• Morris, G., Nevet, A., Arkadir, D., Vaadia, E., & Bergman, H. (2006). Midbrain dopamine neurons encode decisions for future action. Nat Neurosci, 9(8), 1057-1063.
• Nakahara, H., Itoh, H., Kawagoe, R., Takikawa, Y., & Hikosaka, O. (2004). Dopamine neurons can represent context-dependent prediction error. Neuron, 41(2), 269-280.
• Nieoullon, A. (2002). Dopamine and the regulation of cognition and attention. Progress in Neurobiology, 67(1), 53.
• Niv, Y., Daw, N. D., & Dayan, P. (2006). Choice values. Nat Neurosci, 9(8), 987-988.
• Niv, Y., Duff, M. O., & Dayan, P. (2005). Dopamine, uncertainty and td learning. Behav Brain Funct, 1, 6.
• Redish, A. D. (2004). Addiction as a computational process gone awry. Science, 306(5703), 1944-1947.
• Schultz, W. (1999). The reward signal of midbrain dopamine neurons. News Physiol Sci, 14(6), 249-255.
• Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593-1599.
• Schultz, W., & Dickinson, A. (2000). Neuronal coding of prediction errors. Annu Rev Neurosci, 23, 473-500.
• Self, D. (2003). Neurobiology: Dopamine as chicken and egg. Nature, 422(6932), 573-574.
• Suri, R. E. (2002). Td models of reward predictive responses in dopamine neurons. Neural Netw, 15(4-6), 523-533.
• Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The neural basis of loss aversion in decision-making under risk. Science, 315(5811), 515-518.
• Ungless, M. A. (2004). Dopamine: The salient issue. Trends Neurosci, 27(12), 706.

A brief roundup of precent papers on natural rationality

see other news in my Shared Items here or subscribe to the feed

• How monkeys anticipate uncertain future events
  
  
• Cross-cultural studies shows that kinship is the baseline for altruism
• (another similar study points in the same direction)
  
  
• Random copying and innovation are sufficient to explain change in popular culture
• The mirror neurons systems is also sensitive to robotic actions
  
  
• The neural substrates for social events memory formation might have be identified
• Psychologist's Olivier Houdé suggest a "neuropedagogy of reasoning"
  
  
• Children's altruism in the dictator game varies with age and socioeconomic status
• How economics can benefits from neuroeconomics.
  
  

Second Life and agency

For those who, like me, knew almost nothing about Second Life, here is an excellent presentation:




The weirdest thing is that you can surf the web in Second Life world, which is already online ! What is impressive (and maybe scary) is also the virtual economy: millions of dollars transit from our world to this world.

Once your finished, have a look at how Second Life and synthethic worlds make philosophers thinks:
http://homepages.nyu.edu/~dv26/Work/?Artificial_Agency.pdf

I argue that participants in a virtual world such as "Second Life" exercise genuine agency via their avatars. Indeed, their avatars are fictional bodies with which they act in the virtual world, just as they act in the real world with their physical bodies. Hence their physical bodies can be regarded as their default avatars.

I also discuss recent research into "believable" software agents, which are designed on principles borrowed from the character-based arts, especially cinematic animation as practiced by the artists at Disney and Warner Brothers Studios. I claim that these agents exemplify a kind of autonomy that should be of greater interest to philosophers than that exemplified by the generic agent modeled in current philosophical theory. The latter agent is autonomous by virtue of being governed by itself; but a believable agent appears to be governed by a self, which is the anima by which it appears to be animated.

Putting these two discussions together, I suggest that philosophers of action should focus their attention on how we animate our bodies.

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.

Violence, psychopathy, folkspychology and the criminal brain: reflections on the Virginia Tech rampage

It is hard to think about something else today. Again, violence strikes us. Everybody wonders why these things can happen. These behaviors are beyond human understanding. One or two things, however, are worth mentioning. First, everybody, form BBC to CNN to CBC, describes the situation in folk-psychological terms, i.e., the everyday conceptual framework we use to describe and predict actions: X desires Y, A believe that P, etc. When we ask why, intuitively, we look for reasons, for certain beliefs, desires, intentions and motivations that the killer might have in mind beforehand. As philosopher Donald Davidson formulate it :

"If someone acts with an intention then he must have attitudes
and beliefs from which, had he been aware of them and had he the time, he could
have reasoned that his act was desirable. (1980, p. 85)"

The problem with that point of view is that we take those reasons that justifies actions as the cause of the action. All explanations of actions are reduced to identifying the beliefs and desires that cause an action. But what does it tells us? The killer had a desire to kill people, believed that shooting with a gun was a mean to achieve his goal? Is finding the motive enough? Psychopaths, or people suffering from antisocial personality disorder (and I assume that the killer is one) are not normal people, although they may look like:

Psychopaths are not necessarily the sadistic killers of popular fiction. But they lack empathy, and are unable to experience guilt or remorse. They are assertive and egocentric, may be highly manipulative, and are unconcerned by the negative consequences of their actions. When they kill, the crimes are usually well planned and committed for personal gain. But engage in conversation with a psychopath, and he or she might seem perfectly normal. [Nature 410, 296-298 (15 March 2001) Into the mind of a killer]

3-4% of the male population and less than 1% of the female population suffer from this syndrome (Mealey 1995). They lack empathy, show diminished affective reactions, and their prefrontal cortex (where behavior may be inhibited) are reduced in volume by up to 14% (see precedent link and this one). Murderer psychopaths are able to "associate violent and pleasant thoughts with greater ease than other psychopaths or non-psychopathic murderers". Their amydala (a brain area involved in emotional learning, memory and fear, among other, are dysfunctional. They don't learn to fear certain things, such as social reprobation:

The individual is less likely to learn to avoid the use of antisocial behavior to achieve their goals. Instead, the individual may learn to use antisocial behavior instrumentally to achieve their desires (they may receive the potential reward, e.g., financial gain, without the cost of the victim's distress). (Blair, 2005)

Hence psychopaths lack many moral emotions and moral motivations, due to an abnormal brain functioning. There may be a genetic influence, reinforced by the individual's experience. Some studies suggest that genetic screenings of psychopathy would be possible in children. In Hardwired Behavior: What Neuroscience Reveals about Morality Laurence Tancredi review and discuss the case of Ricky Green, a serial killler who murdered 4 people, and sketch an hypothesis on how alcohol, drugs and social environment of Green slowly rewired his genetically predisposed brain and turned it into a cold-blooded murder.

More profoundly, the problem with these behaviors is that they may emerge in any society. Evolutionary biology a population-based science: for many traits, there will be a distribution of traits. A few people are hyper-altruistic, a few others are psychopath, and the big majority is "normal", have social emotions but don't like to be cheated. Sadly, as Mealey concludes, psychopathy is an evolutionary stable strategy. We could expect them in any milieu. Yes, guns should be controlled, but this is not the only solution. Quebec has a gun-control policy stronger than the USA, and yet we had 3 shootings in Montreal since the last 20 years. The important things, I think, is to detect theses persons as soon as possible. Genetic, psychological, behavioral tests, whatever: but people that are born with an abnormal brain functioning that may lead to psychopathy should be treated and helped (psychological tests already exist, actually). Not as criminal, but as sick persons. We don't care about the beliefs and desires that lead to these behavior: maybe the killers in these rampages have 'reasons', they hated everybody, they suffer from social exclusions, they wanted revenge, etc. But that won't help to solve the problems. These people do not process information as we do. I really understood psychopathy the day I watch the movie Kalifornia. The character Brian Kessler (impersonated by David "x-files" Duchovny) reports his tragic meeting with the psychopath Early Grayce (Brad Pitt!):

I'll never know why Early Grayce became a killer. I don' know why any of them did. When I looked into his eyes I felt nothing, nothing. That day I learned any one of us is capable of taking another human life. But I also learned there is a difference between us and them: it's feeling remorse. Dealing with it. Confronting a conscience. Early never did. [...] I remember once going on a school trip to the top of the Empire State Building. When I looked down at the crowds of people on the street they looked like ants. I pulled out a penny and some of us started talking about what would happen if I dropped it from up there and it landed on someone's head. Of course I never crossed that line and actually dropped the penny. I don't think Early Grayce even knew there was a line to cross.

At another place in the movie, the psychopath also say something like "you wonder what goes in my mind when I kill? Nothing". Nothing. They plan they murder, and they do it. Pure rationality without morality, just like predators. No shame, no guilt. They don't have the same 'reasons' if that means anything. Nobody can predict what one person will do. But if we take a "population of brains" perspective, we may be able to understand psychopathy, and find ways to avoid these horrors.

Cognitive Decision-Making: Empirical and Foundational Issues

I recently edited a book at Cambridge Scholars Publishing (UK), entitled "Cognitive Decision-Making: Empirical and Foundational Issues". It is based on a conference I organized in Montreal 2 years ago, Cognitio 2005. Papers in philosophy, psychology, biology and neuroscience discuss different dimension of decision-making. You can read the introduction online, or buy it online. Here is the table of content. Enjoy !
Introduction ix
Benoit Hardy-Vallee

Chapter One 1
Natural Decision
Florian Ferrand

Chapter Two 15
EEG Timing andLibertarianism
Darren Abramson

Chapter Three 25
Statistical Decision and Falsification in Science :
Going Beyond the Null Hypothesis
Dominic Beaulieu-Prévost

Chapter Four 36
Embodied Decisions: Models of Decision Making within a Larger
Cognitive Framework
Terrence C. Stewart

Chapter Five 48
How do Ants and Social Caterpillars Collectively Make Decisions
Audrey Dussutour, Nadia Colasurdo, Stamatios C. Nicolis
and Emma Despland

Chapter Six 66
Spontaneous Decision-Making in Conversation: Variations across Media
Roxanne Beaugh Benoit


Chapter Seven 79
Uncertainty, Risk, and Illusion in Reward Prediction: Evidence from fMRI
Ahmad Sohrabi, Andra M. Smith, Robert L. West and Ian Cameron

Utility and neuroscience: a mechanistic approach of decision-making and rationality

The powerpoint of a talk I gave yesterday:

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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)

Evolutionary cognitive neuroscience and evolutionary neuroeconomics

In a review paper in Evolutionary Psychology(pdf), Krill, Platek, Goetz and Shackelford present an overview of evolutionary cognitive neuroscience (ECN). Whereas evolutionary psychology and cognitive neuroscience tended to study the adapted mind without concerns for the brain, or the brain without concerns for its evolutionary history, this burgeoning field tries to fill the gap. Krill et al. 's paper is a pretty good overview of methods and issues in ECN. Platek and Shackelford, with other researchers, are also publishing two books on ECN:

• Platek, S.M. and Shackelford, T.K. (under contract). Foundations in EvolutionaryCognitive Neuroscience. Cambridge University Press, Cambridge, UK.
• Platek, S.M., Keenan, J.P., and Shackelford, T.K. (Eds.). (2007). Evolutionary Cognitive Neuroscience. Cambridge, MA: MIT Press.

One strenght of ECN is that it does not rely on a functionalist conception of mind, that is, the idea that "what makes something a mental state of a particular type does not depend on its internal constitution, but rather on the way it functions, or the role it plays, in the system of which it is a part."(SEP definition). ECN study the hardware, not the software. Instead of postulating functional modules, ECN try to identify adapted brain mechanisms.

ECN could certainly be a good teacher for neuroeconomics. The Standard View of neuroeconomics assumes that researches in this field explain how humans and other animals follow (or fail to follow) standards of rationality such as decision theory and game theory:

The research topics studied by neuroeconomists fall into two major categories: (i) identifying the neural processes involved in decisions in which standard economic models predict behaviour well; and (ii) studies of ‘anomalies’ where the standard models fail.
(Zak, P. J. (2004). Neuroeconomics. Philos Trans R Soc Lond B Biol Sci, 359(1451), p. 1740)

Neural mechanisms account for the formal rationality of irrationality of behavior. Although this conception is important, I think that another one would be relevant. According to what we might call The Adaptive View, mechanisms uncovered by neuroeconomic researches are (or at least could be) adaptations to ecological conditions. In this view, neural mechanisms account for the natural rationality of behavior. Just as a bird’s wing embodies information about gravity and air viscosity, brain functioning embodies information about the social and physical environment. Robust findings in neuroeconomics can be construed as candidate for adaptive explanations: e.g. if there is a striking difference between risk and ambiguity at the neural level (studies suggest that ambiguity - lack of information about the probability - elicit more activity in the amygdala, cf Hsu et al, Science, 2005), than maybe there is an adaptive function for such a dissociation. Of course, other hypothesis are necessary for these questions, but evolutionary neuroeconomics should be an important research program. Again, more collaboration is needed: the ECN paper do not mention anything about neuroeconomics, and neuroeconomists often neglect evolution.

At the end of the 19th century, American economist Thorstein Veblen wondered "Why is Economics Not an Evolutionary Science" (Veblen, 1898). The reason was that the whole conception of economic agency was based on desire-satisfaction. Veblen argued that economics should be a science of human action, not desire, coherent with biology, anthropology and psychology, not vaguely inspired by physics. One might also ask now "Why is Neuroeconomics Not an Evolutionary Science". Neuroeconomics may be too focused on preferences, and not enough on choice behavior and adaptation.

• Where evolutionary psychology meets cognitive neuroscience: A précis to evolutionary cognitive neuroscience by Krill, A. L., Platek, S. M., Goetz, A. T., and Shackelford, T.K.

On neuroeconomics, its content and its future

I have been contacted by a research group who prepare a report to the Benchmarking Panel of Economics Degrees, to recommend whether Neuroeconomics should be taught as part of Economics degrees at universities across the UK. They had a couple of questions about neuroeconomics, its teaching, its future, etc. My answers are posted here.


What do you feel the teaching of Neuroeconomics gives to its students? (I.e. why would you recommend it to be studied).

• I strongly recommend that any student of economics has a bacground in neuroeconomics.
• Neuroeconomics does what economics should be doing: providing sound models of human valuation before devising more abstract theories.
• It promotes an empirical attitude in economics.
• Neuroeconomics is part of a Trinity: with psychology and classical microeconomics, one has behavioural data, neural activation data and formal models to describe the behavior and the neural activity. A genuine economic science should deals with neurons, peoples and values.

Do you feel the course is challenging to Economics Students? If yes, how so?

• yes, because neuroscience is a highly technical field. Students do not have an empirical background (psychology, neuroscience). Economic departments, however, would benefit from this integration.

Are you pleased with the overall results achieved by your students?

• I taught a course called “Natural Rationality” at the University of Waterloo, and almost half of the content was about neuroeconomics: http://phi673uw.wordpress.com. It triggered many discussions and reflections. So yes, I am pleased with these conceptual “results”.

What potential careers would you feel Neuroeconomics students could succeed in?

• Research in cognitive neuroscience, (not basic neuroscience), experimental economics, microeconomics, sociology, policy-making, Law, politics, psychology, marketing, management.

What potential do you feel Neuroeconomics has for the future of economics in terms of both research and teaching? (I.e. do you feel this will continue to catch on or will the academic world lose interest?)

• Neuroeconomics is not a temporary buzz: it will stay, because it is a complement to behavioural economics. Even if economists lose interest, many researchers from other fields would pursue the project. For the first time in the history of economics, we have – at the same time – formal models of decision-making, cognitive theories of judgment, empirical data on subjects’ real behavior, neural data on brain activation and anatomy, and computational description of neural processes. Economists who take this interdisciplinary turn will revolutionize the discipline. Their students will not discuss axiomatization or existence theorems, but prediction, refutation, correlation, etc. Although it will be hard to keep up to date (there is already many publications), handbooks of neuroeconomics could help research and teaching.

Many sceptics of Neuroeconomics say that the subject only shows where things in the brain happen, and not necessarily why they happen. What would you say to them to convince them otherwise?

• I would first reply that saying that implies discrediting all imaging studies: it is a huge claim. It is always theoretically and practically useful to know what brain resources are involved in an economic task: is it positive/negative emotions, cognition, reward processing, action representation, etc.
• Second, neuroeconomics is not just about imaging: it is also about providing models of neural mechanisms of decision-making, motivation and valuation.
• Third, I would give an example: look at the ultimatum game results. We now from experimental psychology that people make ‘fair’ offer, and reject ‘unfair’ ones. This is compatible with many interpretations (people are irrational, people are influenced by the experimenters, people value fairness). Neuroeconomics (Sanfey et al, 2003) showed that unfair offers trigger negative emotions (anterior insula). Thus it narrows the range of interpretations: people reject unfair offers because they don’t like that. They have preferences for fair offers. The study also showed that other areas are involved: the dorsolateral prefrontal cortex and the anterior cingulated cortex. The first one is involved in cognitve control, the other in emotion modulation. Thus subjects’ choices balance fairness and monetary gain: this processing is not captured by decision theory. Other neural studies also showed that subject enjoy making cooperative moves in prisoner dilemma. Thus subjects value something else than just money. This is important for economics.
  

Many techniques of brain imaging either don’t show enough detail or are potentially damaging to human subjects, or simply cannot be used on humans at all. How do you feel this potential downside of Neuroeconomics will develop, and could it damage the long-term potential for Neuroeconomics to be taught across the board?

• No, because cognitive neuroscience also faces the same problems. Moreover, neuroeconomics also relies on lesion studies, animal models and computational modelling. Again, Handbooks of neuroeconomics could be use as “databases” for research in economics.

Please add any further comments:

• The keyword for the future of economics is interdisciplinarity: neuroscience, psychology, biology, Artificial Intelligence and anthropology are all necessary if we want economics to be the “science which studies human behavior as a relationship between ends and scarce means which have alternative uses” (L. Robbins). Formal models can represent human behavior, but these models ought to be grounded in facts, something these disciplines can afford.

The Ultimatum Game Outside The Lab

In the ultimatum game, a ‘proposer’ (Alice) makes an offer to a ‘responder’ (Bob) that can either accept or refuse the offer. The offer is a split of an amount of money. If Bob accepts, he keeps the offered amount while Alice keeps the difference. If Bob rejects it, however, both players get nothing. According to game theory, rational agents must behave as follows: Alice should offer the smallest amount possible, in order to keep as much money as possible, and Bob should accept any proposed amount, because a small amount should be better than nothing. Thus if there is $10 to split, Alice should offer $1 and keep $9, while Bob should accept the split.
The ultimatum game has been studied in many contexts where different parameters of the game were modified: culture, age, sex, the amount of money, the degree of anonymity, the length of the game, and so on. The results show a robust tendency: the game-theoretic strategy is rarely played, because people tend to make fair’ offers. While proposers offer about 50% of the amount, responders tend to accept these offers while rejecting most of the ‘unfair’ offers (less than 20%). Since the rules of the game are quite simple, subject do not deviate from optimal strategy because they did not understand them. Rather, normal subjects seem to have a tendency to cooperate and value fairness

In a newspaper version of the ultimatum, researchers found that "older participants and women care more about equal distributions and that Internet users are more self-regarding than those using mail or fax". The results, however, confirmed that standard results are valid outside the lab.