Simple heuristics as equilibrium strategies in mutual sequential mate search

In this paper, we study whether simple heuristics can arise as equilibrium strategies in mutual sequential mate search. To this aim, we extend the mate search model of Todd and Miller (1999), involving an adolescence (learning) phase followed by an actual mating phase, to a strategic game where the players, as the individuals in the mating population, choose before starting the adolescence phase, the best rule - among the four available search (aspiration adjustment) rules - to maximize their likelihood of mating, given the choice of other individuals. Conducting Monte Carlo simulations, we show that the use of the Take the Next Best Rule by the whole population never becomes a (Nash) equilibrium in the simulation range of adolescence lengths. While the unanimous use of the Adjust Relative Rule by the whole population arises as an equilibrium for a wide part of the simulation range, especially for medium to high adolescence lengths, the rules Adjust Up/Down and Adjust Relative/2 are unanimously chosen as equilibrium strategies for a small part of the simulation range and only when the adolescence is long and short, respectively

Simulating the Mutual Sequential Mate Search Model under Non-homogenous Preferences

This paper extends the Todd and Miller's (1999) mutual sequential mate search model with homogenous preferences to the case of non-homogenous preferences. Our simulations show that the size of heterogeneity in the preferences affects the performance rankings -as well as the absolute success levels- of the mate search heuristics in the model with respect to both mating likelihood and mating stability

Simulating the Mutual Sequential Mate Search Model under Non-homogenous Preferences

This paper extends the Todd and Miller's (1999) mutual sequential mate search model with homogenous preferences to the case of non-homogenous preferences. Our simulations show that the size of heterogeneity in the preferences affects the performance rankings -as well as the absolute success levels- of the mate search heuristics in the model with respect to both mating likelihood and mating stability

A New Heuristic in Mutual Sequential Mate Search

In this paper, we propose a new heuristic to be used as a mate search strategy in the Todd and Miller's (1999) human mate choice model. This heuristic, which we call Take the Weighted Average with the Next Desiring Date, is a plausible search rule in terms of informational assumptions, while in terms of mating likelihood it is almost as good as the most successful, yet also unrealistic, heuristic of Todd and Miller (1999), namely the Mate Value-5 rule, which assumes that agents in the mating population completely know their own mate values before interacting with any date. The success of our heuristic stems from its extreme power to lead an average agent in the mating population to always underestimate his/her own mate value during the adolescence (learning) phase of the mating process. However, this humble heuristic does not perform well in terms of marital stability. We find that the mean within-pair difference is always higher under our heuristic (possibly due to high estimation errors made in the learning phase) than under any heuristic of Todd and Miller (1999). It seems that becoming ready to pair up with agents whose mate values are well below one's own mate value pays off well in the mating phase but also incurs an increased risk of marital dissolution

Behavioural Economics: Classical and Modern

In this paper, the origins and development of behavioural economics, beginning with the pioneering works of Herbert Simon (1953) and Ward Edwards (1954), is traced, described and (critically) discussed, in some detail. Two kinds of behavioural economics – classical and modern – are attributed, respectively, to the two pioneers. The mathematical foundations of classical behavioural economics is identified, largely, to be in the theory of computation and computational complexity; the corresponding mathematical basis for modern behavioural economics is, on the other hand, claimed to be a notion of subjective probability (at least at its origins in the works of Ward Edwards). The economic theories of behavior, challenging various aspects of 'orthodox' theory, were decisively influenced by these two mathematical underpinnings of the two theoriesClassical Behavioural Economics, Modern Behavioural Economics, Subjective Probability, Model of Computation, Computational Complexity. Subjective Expected Utility

The Success of the Deferred Acceptance Algorithm under Heterogenous Preferences with Endogenous Aspirations

In this paper, we consider a one-to-one matching model with two phases; an adolescence phase where individuals meet a number of dates and learn about their aspirations, followed by a matching phase where individuals are matched according to a version of Gale and Shapley's (1962) deferred acceptance (DA) algorithm. Using simulations of this model, we study how the likelihoods of matching and divorce, and also the balancedness and the speed of matching associated with the outcome of the DA algorithm are affected by the size of correlation in the preferences of individuals and by the frequency individuals update their aspirations in the adolescence phase

Emergence and Stability of Self-Evolved Cooperative Strategies using Stochastic Machines

To investigate the origin of cooperative behaviors, we developed an evolutionary model of sequential strategies and tested our model with computer simulations. The sequential strategies represented by stochastic machines were evaluated through games of Iterated Prisoner's Dilemma (IPD) with other agents in the population, allowing co-evolution to occur. We expanded upon past works by proposing a novel mechanism to mutate stochastic Moore machines that enables a richer class of machines to be evolved. These machines were then subjected to various selection mechanisms and the resulting evolved strategies were analyzed. We found that cooperation can indeed emerge spontaneously in evolving populations playing iterated PD, specifically in the form of trigger strategies. In addition, we found that the resulting populations converged to evolutionarily stable states and were resilient towards mutation. In order to test the generalizability of our proposed mutation mechanism and simulation approach, we also evolved the machines to play other games such as Chicken, Stag Hunt, and Battle, and obtained strategies that perform as well as mixed strategies in Nash Equilibrium.Comment: 8 pages, 5 figures, Submitted to and Accepted for IEEE SSCI 2020 (Symposium Series on Computational Intelligence