Journal of Theoretical Biology 378 (2015) 96–102 Contents lists available at ScienceDirect Journal of Theoretical Biology journal homepage: www.elsevier.com/locate/yjtbi Evolutionary dynamics of group fairness Fernando P. Santos a,b, Francisco C. Santos a,b, Ana Paiva a, Jorge M. Pacheco c,d,b,n a INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, IST-Tagusparque, 2744-016 Porto Salvo, Portugal b ATP-group, Instituto de Investigação Interdisciplinar, P-1649-003 Lisboa Codex, Portugal c Centro de Biologia Molecular e Ambiental, Universidade do Minho, 4710-057 Braga, Portugal d Departamento de Matemática e Aplicações, Universidade do Minho, 4710-057 Braga, Portugal HIGHLIGHTS Agreements often involve group decisions where fairness may play a key role. This can be addressed through a multiplayer generalization of the Ultimatum Game. We show that imposing consensuses raises the proposals, leading to fairer agreements. Stochastic effects are shown to further enhance group fairness. article info abstract Article history: The emergence and impact of fairness is commonly studied in the context of 2-person games, notably Received 22 March 2015 the Ultimatum Game. Often, however, humans face problems of collective action involving more than Accepted 21 April 2015 two individuals where fairness is known to play a very important role, and whose dynamics cannot be Available online 30 April 2015 inferred from what is known from 2-person games. Here, we propose a generalization of the Ultimatum Keywords: Game for an arbitrary number of players – the Multiplayer Ultimatum Game. Proposals are made to a Behavioral dynamics group of responders who must individually reject or accept the proposal. If the total number of Collective action individual acceptances stands below a given threshold, the offer will be rejected; otherwise, the offer Cooperation will be accepted, and equally shared by all responders. We investigate the evolution of fairness in Ultimatum game populations of individuals by means of evolutionary game theory, providing both analytical insights and Evolutionary game theory results from numerical simulations. We show how imposing stringent consensuses significantly increases the value of the proposals, leading to fairer outcomes and more tolerant players. Furthermore, we show how stochastic effects – such as imitation errors and/or errors when assessing the fitness of others – may further enhance the overall success in reaching fair collective action. & 2015 Elsevier Ltd. All rights reserved. 1. Introduction what happens when two individuals interact, also when groups get together to make collective decisions, the concept of fairness is Although evidence exists of collective action problems that other known to play a very important role (Fehr and Schmidt, 1999; Fehr species engage on and successfully solve (Axelrod and Hamilton, and Gächter, 2000; Henrich et al., 2001; Rabin, 1993). However, and 1981; Maynard-Smith and Price, 1973), humans are truly singular in in sharp contrast to the two-person interaction cases, where fairness the extent to which they resort to collective action (Barrett, 2003; has been given considerable attention, both theoretically (Binmore, Hardin, 1968; Henrich et al., 2001; Kollock, 1998; Levin, 2012; 1998; Gintis et al., 2003; Nowak et al., 2000; Page and Nowak, 2002) Ostrom, 1990). To this end they have developed highly sophisticated and experimentally (Fehr and Schmidt, 2006; Henrich et al., 2001, and unparalleled mechanisms (Apicella et al., 2012; Dietz et al., 2003; 2010; Rand et al., 2013; Sanfey et al., 2003), the study of fairness in Fehr and Fischbacher, 2003; Fowler and Christakis, 2010; Gintis, connection to group decisions has been limited to few investigations 2000; Kollock, 1998; Nowak and Sigmund, 2005; Ohtsuki and Iwasa, (Bornstein and Yaniv, 1998; Fischbacher et al., 2009; Messick et al., 2004; Pacheco et al., 2009; Skyrms, 2004; Skyrms, 2010). Similar to 1997; Robert and Carnevale, 1997). The Ultimatum Game (UG)(Güth et al., 1982) has constituted the framework of choice (for an exception, see (Van Segbroeck n et al., 2012)) with which to address the emergence and evolution Corresponding author at: Departamento de Matemática e Aplicações, #Universidade do Minho, 4710-057 Braga, Portugal. Tel.: þ351 253 604 364. of fairness in two-person interactions. In the two-person UG (and E-mail address: [email protected] (J.M. Pacheco). using common notation (Sigmund, 2010)), one individual plays the http://dx.doi.org/10.1016/j.jtbi.2015.04.025 0022-5193/& 2015 Elsevier Ltd. All rights reserved. F.P. Santos et al. / Journal of Theoretical Biology 378 (2015) 96–102 97 role of the Proposer whereas the other individual plays the role of noting that the maximum payoff of individual i is obtained when pi is the Responder. The Proposer is endowed with an amount (without thesmallestpossibleandallotherpk (the opponents' offers) are loss of generality, we may assume it is 1 unit) and makes an offer, maximized. Therefore, the pressure to free-ride, that is, offering less which is a fraction p of that amount. We may assume that the and expecting that others will contribute, should be very high. Responder has an expectation value q, and will accept the proposal Furthermore, dividing the game in two stages and reasoning in a if the condition pZq is satisfied, in which case the Proposer earns backward fashion, the conclusions regarding the sub-game perfect 1Àp and the Responder earns p. In such a scenario, a fair offer equilibrium of this game anticipate the use of the smallest possible pi corresponds to an equal split, that is p¼1/2. and qi, irrespectively of N and M, thus mimicking the conclusions for Theoretical (Gale et al., 1995; Nowak et al., 2000; Page and the traditional 2-person UG. Following the well-mixed assumption, Nowak, 2002; Rand et al., 2013) and experimental (Henrich et al., the fitness of one individual will be provided by the average payoff 2004; Henrich et al., 2001) investigations have shown that humans calculated over a big number of groups, randomly sampled from the generally accept offers with pZ0.4, a feature which (with some population. variation) applies to both old and new societies, as a series of cross- culture studies have shown (Henrich et al., 2001). But what about 2.2. Evolutionary dynamics group decisions? While there are no theoretical studies of the UG in a group context, there is one experiment (Messick et al., 1997)which Instead of revising their strategies through rational reasoning, naturally captures some limiting situations of the Multi-Player UG Humans often resort to the experiences and successes of others to (MUG) that we develop below, and which puts in evidence a small select their next move, as, in fact, has been shown to be the case in part of the plethora of new possibilities enacted by group decisions, the context of public donations (Carman, 2003; Fowler and Christakis, with direct consequences in what concerns the concept of fairness. 2010; Rees et al., 2009). Such an interacting dynamical process, Naturally, we shall take the opportunity to compare our independent grounded on peer-influence and imitation, creates a behavioral theoretical predictions with available results from the aforemen- ecosystem in which strategies and behaviors evolve in time, whereas tioned behavioral experiments. the returns of each individual depend on the actual frequency of each strategy present in the population. Here we adopt such social learning 2. Model and methods dynamics (Rendell et al., 2010; Santos and Pacheco, 2011; Sigmund et al., 2010; Tomasello and Call, 1997; Traulsen et al., 2006), which is 2.1. Multiplayer ultimatum games also well suited to be used in the framework of evolutionary game theory. The baseline assumption is that individuals performing better Let us assume a large population of individuals, who assemble when playing multiplayer UG (i.e. those achieving higher average into groups of size N. Each individual has a strategy encoded by 2 real payoffs) will be more often imitated and thus their behaviour will numbers fp; qgA½0; 1 , whose significance is explained below. At any spread in the population. In other words, social success drives the instance of the game, there will be 1 Proposer and NÀ1 Responders. evolution of strategies in the population. This dynamical process is Following the conventional notation of UG (Sigmund, 2010), the total implemented both analytically and in computer simulations. amount initially given to the Proposer is equal to 1. The Proposer will A offer an amount p [0,1] corresponding to her/his strategy, whereas 2.3. Simulations each of the NÀ1 Responders will accept or reject the offer based on A each one's individual acceptance threshold given by q [0,1]. Thus, Our simulations start from a well-mixed population of size r each Responder will individually state his acceptance (if q p)or Z¼1024,muchlargerthanthegroupsizeN.Individualstrategy 4 rejection (if q p). Overall, the group acceptance is conditional on a values of p and q are all drawn from a probability distribution minimum number of individual acceptances (M): if the number of obtained by discretizing a uniform distribution in the interval [0,1] individual acceptances stands below M, the offer will be rejected. into 101 equally spaced values. Simulations last for 4000 generations, Otherwise, the offer will be accepted. In the former case, no one and we consider that, in each generation, all the individuals have the earns anything, whereas in the latter case, the Proposer will keep opportunity to revise their strategy through imitation. At every 1Àp to himself and the group will share the remainder, that is, each (discrete and asynchronous) time step, two individuals A and B are Responder gets p/(NÀ1). Thus, in line with the UG, a fair split will be randomly selected from the population and their individual fitness is characterized by p¼1À1/N,asinthiscasebothProposer and computed, by averaging the payoff obtained from 104 randomly Responders will get the same fraction of the endowment.