The oeconomy of nature is in this respect exactly of a piece with what it is upon many other occasions. With regard to all those ends which, upon account of their peculiar importance, may be regarded, if such an expression is allowable, as the favourite ends of nature, she has constantly in this manner not only endowed mankind with an appetite for the end which she proposes, but likewise with an appetite for the means by which alone this end can be brought about, for their own sakes, and independent of their tendency to produce it. Thus self-preservation, and the propagation of the species, are the great ends which Nature seems to have proposed in the formation of all animals. Mankind are endowed with a desire of those ends, and an aversion to the contrary; with a love of life, and a dread of dissolution; with a desire of the continuance and perpetuity of the species, and with an aversion to the thoughts of its intire extinction. But though we are in this manner endowed with a very strong desire of those ends, it has not been intrusted to the slow and uncertain determinations of our reason, to find out the proper means of bringing them about.
- Adam Smith, (1759)
One of the main goal of this blog (and the research that feeds it) is the development of a coherent and rigourous naturalized theory of rationality--or more precisely, a theory of natural rationality (I'll discuss the difference in another post). This theory-in-progress construes rationality as a natural feature of the biological world (and yes, fellows philosophers, I deal with the question of normativity, but another day). The big picture is the following: there is a distiction between rational competence and rational performance (as in linguistics): the competence is the set of mechanisms that make rational performance (= rational actions) possible. Neuroeconomics, as I see it, is the most promising research program that attempt to decipher the rational competence. An interesting possibility raised by these researches is that neural mechanisms involved in rational comptence may not be uniquely humans. In my phd thesis (in French, pdf
here), I proposed that the whole vertebrate clade should be considered as the natural kind that implements the category "rational agents". I blogged a lot about neuroeconomics (competence), so today I'll talk about
performance. How do animals behave rationally? In a basic, utility-maximizing sense: they optimize a utility function. While research in behavioral ecology showed that this hypothesis is justified, neureoconomics shows that it is more than an 'as-if' hypothesis or a useful fiction. So let's talk about behavioral ecology and rational performance.
Behavioral ecology models animals as economic agents that achieve ultimate goals (survival and reproduction) through instrumental ones (partner selection, food acquisition and consumption, etc.)[1]. Optimal foraging theory, for instance, represents foraging as a maximization of net caloric intake. With general principles derived from microeconomics, optimization theory and control theory, coupled with information about the physical constitution and ecological niche of the predator, it is possible to predict what kind of prey and patch an animal will favor, given certain costs such as search, identification, procurement, and handling costs. Optimal foraging theory (OFT), as their founders suggested, tries to determine “which patches a species would feed and which items would form its diet if the species acted in the most economical fashion”[2]. OFT models primarily animals as efficient goal-seekers and goal-achievers.
OFT thus incorporates agents, their choices, the currency to be maximized (most of the time a caloric gain) and a set of constraints. Most researches study where to forage (patch choice), what to forage (prey choice) and for how long (optimal time allocation). It is supposed that the individual animal make a series of decisions in order to solve a problem of sequential optimization. An animal looking for nutrients must maximize its caloric intake while taking into account those spent in seeking and capturing its prey; to this problem one must also add, among others, the frequency of prey encounter, the time devoted to research and the calories each prey type afford. All these parameters can be represented by a set of equations from which numerical methods such as dynamic programming allow biologists to derive algorithms that an optimal forager would implement in order to maximize the caloric intake. These algorithms are used afterward for the prediction of the behavior. Mathematically speaking, OFT is the translation of decision theory axioms—together with many auxiliary hypotheses—into tractable calories-maximization algorithms.
Economic models of animal behavior succeeded in explanation and prediction. It predicts for example how birds split their time between defending a territory and foraging[3], or between singing and foraging[4]. In their meta-analysis, Sih and Christensen[5] re-examined 134 foraging studies in laboratory and natural context, experimental or observational, and concluded that, although predictive success is not perfect, the predictivity of the theory is relatively high when preys are motionless (the prey can be a plant, seeds, honey, etc).
Interactive contexts are aptly modeled by game theory, mainly social foraging, fighting and predatory-preys relations[6]. For example, a model of Vickery et al[7] predicted that the co-occurrence of three social foraging strategies, producer (gathering nutrients) scrounger (stealing nutrients) and opportunist (switching between producer and scrounger) occurs only in the—very improbable—case where the losses opportunists would incur while foraging would be exactly equivalent to the profit of stealing. The model, however, predicts certain distributions of pairs of strategies that constitute evolutionary stable strategies (ESS), that is, a strategy that cannot be invaded by any competing alternative strategy. The proportion of food patch shared by scroungers, the size of the group and the degree of compatibility between the scrounger and producer strategy (i.e., if it is easy for the animal to perform both activities) determine the distribution of the strategies in a population, which was confirmed, inter alia, in birds (Lonchura punctulata)[8]. As predicted by the model, the producer strategy becomes less common when the cost of individual foraging increases.
Recently, behavioral ecologists found that animals could also be modeled as traders in biological markets. Obviously, biological markets do not have symbolic and conventional currencies systems, but in many interactions between animals institute trading structures. As soon as agents are able to provide commodities for mutual profit, the competition for obtaining commodities creates a higher bid. Animals seek and select partners according to the principle of supply and demand in interspecific mutualism, mate selection and intraspecific co-operation. An example of the last type is the cleaning market instituated by Hipposcarus harid fishes and cleaners-fishes Labroides dimidiatus. The “customers” (Hipposcarus) use the services of the cleaner to have its parasites removed, whereas the cleaners, occasionally, cheat and eat the healthy tissues of its customers. Since the cleaners offer a service that cannot be found elsewhere, they benefit from a certain economic advantage. A customer cannot choose to be exploited or not, whereas the cleaner chooses to cooperate or not (thus the payoffs are asymmetric). The customer—a predator fish that could eat the cleaner—abstains from consuming the cleaner in the majority of the cases, given the reciprocal advantage. Bshary and Schaffer[9] observed that cleaners spend more time with occasional customers than with regular ones and fight for them, since occasional customers are easier to exploit. All this makes perfect economic sense.
One could of course reformulate each of these results, and put the words decision or exchange between quotation marks to imply that they are mere façons de parler and not really decisions and exchanges, but instinctive behaviors preserved by natural selection. If this is the case, we should also put quotation marks when we talk about human beings: human behavioral ecology applies the same bio-economic logic with the same success to humans. Agents are modeled as optimal forager subjects to a multitude of constraints. Given available resources in the environment of a community, one can generates a model that predicts the optimal allocation of resources. These models are of course more complex than animal ones since they integrate social parameters like local habits, technology or economic structures. Models of human foraging where able for instance to explain differences in foraging style between tribes in the Amazonia, given the distance to be traversed and the technology used[10]. Food sharing, labor division between men and women, agricultural cultures and even Internet browsing (where the commodity is information) can be modeled by human behavioral ecology[11]. Hence even if foraging or trading behaviors are merely the execution of adaptations, the fact remains that their performance is best described as a decision-making process.
[1] (Krebs & Davies, 1997; Pianka, 2000)
[2] (MacArthur & Pianka, 1966, p. 603).
[3] (Kacelnik, Houston, & Krebs, 1981),
[4] (Thomas, 1999)
[5] (Sih & Christensen, 2001)
[6] (Hansen, 1986; Lima, 2002).(Dugatkin & Reeve, 1998)
[7] (Vickery, Giraldeau, Templeton, Kramer, & Chapman, 1991)
[8] (Mottley & Giraldeau, 2000)
[9] (Bshary & Schaffer, 2002)
[10] (Hames & Vickers, 1982)
[11] (Jochim, 1988; Kaplan, Hill, Hawkes, & Hurtado, 1984; Pirolli & Card, 1999)
Bshary, R., & Schaffer, D. (2002). Choosy reef fish select cleaner fish that provide high-quality service. Animal Behaviour, 63(3), 557.
Dugatkin, L. A., & Reeve, H. K. (1998). Game theory & animal behavior. New York ; Oxford: Oxford University Press.
Hames, R. B., & Vickers, W. T. (1982). Optimal Diet Breadth Theory as a Model to Explain Variability in Amazonian Hunting. American Ethnologist, 9(2, Economic and Ecological Processes in Society and Culture), 358-378.
Hansen, A. J. (1986). Fighting Behavior in Bald Eagles: A Test of Game Theory. Ecology, 67(3), 787-797.
Jochim, M. A. (1988). Optimal Foraging and the Division of Labor. American Anthropologist, 90(1), 130-136.
Kacelnik, A., Houston, A. I., & Krebs, J. R. (1981). Optimal foraging and territorial defence in the Great Tit (Parus major). Behavioral Ecology and Sociobiology, 8(1), 35.
Kaplan, H., Hill, K., Hawkes, K., & Hurtado, A. (1984). Food Sharing Among Ache Hunter-Gatherers of Eastern Paraguay. Current Anthropology, 25(1), 113-115.
Krebs, J. R., & Davies, N. B. (1997). Behavioural ecology : an evolutionary approach (4th ed.). Oxford, England ; Malden, MA: Blackwell Science.
Lima, S. L. (2002). Putting predators back into behavioral predator-prey interactions. Trends in Ecology & Evolution, 17(2), 70.
MacArthur, R. H., & Pianka, E. R. (1966). On optimal use of a patchy environment. American Naturalist(100), 603-609.
Mottley, K., & Giraldeau, L. A. (2000). Experimental evidence that group foragers can converge on predicted producer-scrounger equilibria. Anim Behav, 60(3), 341-350.
Pianka, E. R. (2000). Evolutionary ecology (6th ed.). San Francisco, Calif.: Benjamin Cummings.
Pirolli, P., & Card, S. (1999). Information Foraging. Psychological Review, 106(4), 643.
Sih, A., & Christensen, B. (2001). Optimal diet theory: when does it work, and when and why does it fail? Animal Behaviour, 61(2), 379.
Smith, A. ([1759] 2002). The theory of moral sentiments. Cambridge, U.K. ; New York: Cambridge University Press.
Thomas, R. J. (1999). Two tests of a stochastic dynamic programming model of daily singing routines in birds. Anim Behav, 57(2), 277-284.
Vickery, W. L., Giraldeau, L.-A., Templeton, J. J., Kramer, D. L., & Chapman, C. A. (1991). Producers, Scroungers, and Group Foraging. American Naturalist, 137(6), 847-863.
