Hacienda Pública Española / Review of Public Economics, 200-(1/2012): 131-146 © 2012, Instituto de Estudios Fiscales Evolutionary Stability of Kantian Optimization* PHILIP A. CURRY University of Waterloo JOHN E. ROEMER Yale University Abstract In Nash equilibrium, agents are autarchic in their optimization protocol, whereas in Kantian equi­ librium, they optimize in an interdependent way. Typically, researchers into the evolution of homo economicus treat preferences as being determined by selective adaptation, but hold fixed the opti­ mization protocol as autarchic. Here, we ask whether natural selection might choose the optimizing protocol to be either autarchic or interdependent. That is, will Kantian players, for whom the stable concept is Kantian equilibrium drive Nash players (for whom the stable concept is Nash equilibri­ um) to extinction, or otherwise? The answer depends upon whether players can signal their type to others. Keywords: Kantian Equilibrium, Nash Equilibrium, Evolutionary Stable Strategy, Cooperation. JEL classification: C73, C62, D64. 1. Introduction There exists a literature in economics that considers some of the characteristics of agents, normally taken to be primitives, as being shaped by the forces of natural selection. This literature considers preferences 1 to be a heritable trait, and then examines how homo economicus would have evolved. The present paper examines the notion of Kantian opti­ mization in strategic environments as developed in Roemer (1996, 2010, 2012) and asks whether there may be an evolutionary advantage to such thinking, particularly in relation to the autarchic optimization of Nash equilibrium. We find that Kantians hold an advantage over Nash players as long as they can identify whom they are playing against; absent this as­ sumption, nature would select for Nash optimizers. In particular, we examine the evolutionary dynamic in two games: the Prisoners’ Dilem­ ma and the Random Dictator Games. While players choose their strategies when playing these games, from an evolutionary standpoint, the meta-strategies are to behave as a Nash 132 PHILIP A. CURRY AND JOHN E. ROEMER optimizer or a Kantian optimizer. We then look for Evolutionarily Stable Strategies (ESS’s) as in Maynard Smith (1982, 1989). The determination of the type of player may arise from hard-wiring, or it may be a choice made by the individual based on past experiences and ob­ servations. In general, we are agnostic about the specifics concerning the determination of type, as the two forms of evolution operate in a similar fashion. 2. Kantian Equilibrium The concept of Kantian equilibrium in games was introduced (see Roemer[1996, 2010, 2012]) to model the view that individuals in a society might apply the Kantian categorical imperative when deciding upon their actions. One of the simplest examples is that of a soci­ ety of fishers, who own in common a lake, upon which they fish. Each fisher has preferences over fish consumed and labor expended in fishing, represented by a concave utility function ui(x,L). The total fish caught on the lake will be G(∑Li), where G is a strictly concave pro­ duction function of total labor expended. The catch going to fisher i will be proportional, sta­ tistically speaking, to the labor he expends, that is: Li xLi ( 1 ,,… Ln ) = G Lj . (0.1) j ()∑ ∑ L Thus a game is defined whose strategies are fishing times and whose payoff func­ tions are VLi ( 1 ,,… Ln )(= uxLi( i 1 ,,… LLn ) ,.i ) (0.2) It is well-known that, since G is strictly concave, the Nash equilibria of this game are Pareto inefficient; there is over-fishing, as each fisher ignores the congestion effect of his be­ havior on the productivity of labor on the lake, which is decreasing in total fishing time. Suppose, however, that fishers reason in the following way. When facing a proposed labor vector L = (L1,…,Ln), fisher i thinks: “I have a right to increase (or decrease) my labor by a factor r if and only if I would be happy if all other fishers did so as well.” An equilib­ rium, with respect to this kind of behavior, is a vector L such that nobody would like every­ body to alter his behavior by some factor r ≠ 1. The formal definition is: Definition 1. A vector L = (L1,…,Ln) is a multiplicative Kantian equilibrium of the fish­ er game V if and only if: ()∀j ar g ma x Vi (rL1 ,,… r Ln ) =1 (0.3) ( r≥0 ) It is easy to prove that if a strictly positive vector L is a multiplicative Kantian equilib­ rium of the fisher game, then it is Pareto efficient (Roemer [2010]). In other words, Kantian behavior in the sense described solves the tragedy of the commons. Evolutionary Stability ok Kantian Optimization 133 Elinor Ostrom (1990) has studied many small societies like the fisher economy, and dis­ covers that they do not play the Nash equilibrium, but rather regulate fishing times in order to achieve Pareto efficient outcomes. These outcomes are ones in which each fisher keeps his catch. So they are efficient outcomes in which output (fish) is distributed in proportion to labor expended. It can be proved that any such allocation is a multiplicative Kantian equilibrium of the ‘game’ being played by fishers (see Roemer [2012]). The question arises: Have the fishers in these economies discovered the Kantian way of thinking? Does Kantian optimization play a role in maintaining these equilibria? We do not know. However, it is worth considering whether there may be an evolutionary explanation for the use of Kantian optimization. 3. Natural Selection In this section, we consider a large population of agents who are pair-wise matched at ran­ dom each period to play a game; a fraction π of these agents optimize in the Nash manner, and 1 – π optimize in the Kantian manner. We examine the expected payoff to each agent under two regimes. In the first regime, an agent’s type is observable to his opponent. In the second, an agent’s type is private knowledge. A discussion of these two regimes follows in section 4. We consider payoffs to be proportional to evolutionary fitness, so that the type with the higher expected payoff will grow at a greater rate within the population. In this fashion, we can consider how a player optimizes and we examine whether an Evolutionarily Stable Strat­ egy (ESS) exists, as in Maynard Smith (1982, 1989). We say that an ESS exists if either the expected payoff to Nash optimizers is greater when π is close to 1, or when the expected pay- off to Kantian optimizers is greater when π is close to 0. In the former case, the population is almost all Nash optimizers and a mutant Kantian would do worse than the Nash players and so be driven to extinction. In the latter case, the population is almost all Kantian opti­ mizers, and a Nash optimizer would do worse than the Kantians and so be driven out. In the following two sections, we consider whether an ESS exists in two two-person games: the prisoners’ dilemma, and the random dictator game. 3.1. The Prisoners’ Dilemma Game Consider the prisoners’ dilemma (PD) game: Cooperate Defect Cooperate (1,1) (a,b) Defect (b,a) (0,0) where b > 1 and a < 0. Suppose that the Row player plays Cooperate with probability p, and the Column player Cooperates with probability q. Then the payoff functions of the two play­ ers are: 134 PHILIP A. CURRY AND JOHN E. ROEMER VR ( p,q ) = pq + p (1− qa) + (1 − pq) b (1.1) V C ( p, q ) = pq + p(1 − q)bb + (1 − p)qa A multiplicative Kantian equilibrium of this game is a strategy pair (p,q) such that arg maxVrR ( p,r q) ==1 arg maxV C (rp,r q) , (1.2) r r 1 1 where r ranges over the interval [0,min[ /p, /q]]. We have: • Proposition 1 A. (1,1) is a Kantian equilibrium of the PD game if and only if (a + b) ≤ 2, and in this case, there is no other (non-trivial) Kantian equilibrium 2. B. If a + b > 2, then the unique (non-trivial) Kantian equilibrium is given by ab+ p * = q* = <1. 2 (ab+ −1) In particular, p* > 1/2. Proof: See the appendix. Proposition 1 gives us the payoffs when two Kantian optimizers play each other. We know that the only Nash equilibrium in the PD game is p = q = 0, since playing Defect is a strictly dominant strategy. We suppose that if a Kantian player knows that he faces a Nash player, he optimizes à la Nash: there is no point using the Kantian protocol as he knows the opponent will not use it. We first observe: • Proposition 2 Suppose each player can identify the type of his opponent. Then the Kantian play­ ers will drive the Nash players to extinction. Proof: 1. If there is a match between two Kantian players, Proposition 1 tells us what the equi­ librium is. The payoff to both players is positive regardless of the values of (a,b). 2. A Nash player always plays p = 0. So the payoff to two matched Nash players is zero. 3. Suppose a Nash player faces a Kantian player. Then they play p = q = 0 and their payoffs are each zero. 4. In sum, Nash players always receive zero payoffs in these matches, and Kant­ ian players sometimes receive zero payoffs and sometimes positive payoffs. Consequently, on average, they are more fit than Nash players, and drive the Nash players to extinction. Evolutionary Stability ok Kantian Optimization 135 We now study the case when matched players cannot identify the type of their oppo­ nent.