Erika Chiba’s research while affiliated with Nagoya University and other places

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Publications (5)


Figure 2. Snapshots of a simulation. Cooperators (defectors) are shown in blue (red). Initially (t = 0), cooperators and defectors are fifty-fifty. When uniform movements (α = 0) are assumed, cooperators die out. In contrast, cooperators spread when Lévy flights or fixed movements are assumed. At t = 50, cooperators almost go extinct but a few cooperative clusters survive in the two cases. Finally, the cluster of cooperators can invade the sea of defectors. We used the PD game where (R, S, T, P) = (1, −0.4, 1.4, 0). L = 100 and ρ = 2/3.
Figure 4. Fraction of cooperators ¯ f Call as a function of sensitivity s when α is varied where ¯ f Call is obtained by averaging all ¯ f C in the whole parameter ranges (0 ≤ T ≤ 2 and −1 ≤ S ≤ 1). For each point on the lines, 10 simulation runs are averaged. Cooperation was promoted the most in the moderate sensitivity s = 1/2.
Figure 6. Fraction of cooperators ¯ f C as functions of densities and sensitivity. Top: Lévy flights (α = 3.0). Center: Uniform movements (α = 0.0). Bottom: Fixed movements (P(1) = 1). PD game with (R, S, T, P) = (1, −0.4, 1.2, 0) was used. We averaged 10 simulation runs for each data point.
How Lévy Flights Triggered by the Presence of Defectors Affect Evolution of Cooperation in Spatial Games
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August 2022

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56 Reads

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2 Citations

Artificial Life

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Erika Chiba

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Cooperation among individuals has been key to sustaining societies. However, natural selection favors defection over cooperation. Cooperation can be favored when the mobility of individuals allows cooperators to form a cluster (or group). Mobility patterns of animals sometimes follow a Lévy flight. A Lévy flight is a kind of random walk but it is composed of many small movements with a few big movements. The role of Lévy flights for cooperation has been studied by Antonioni and Tomassini, who showed that Lévy flights promoted cooperation combined with conditional movements triggered by neighboring defectors. However, the optimal condition for neighboring defectors and how the condition changes with the intensity of Lévy flights are still unclear. Here, we developed an agent-based model in a square lattice where agents perform Lévy flights depending on the fraction of neighboring defectors. We systematically studied the relationships among three factors for cooperation: sensitivity to defectors, the intensity of Lévy flights, and population density. Results of evolutionary simulations showed that moderate sensitivity most promoted cooperation. Then, we found that the shortest movements were best for cooperation when the sensitivity to defectors was high. In contrast, when the sensitivity was low, longer movements were best for cooperation. Thus, Lévy flights, the balance between short and long jumps, promoted cooperation in any sensitivity, which was confirmed by evolutionary simulations. Finally, as the population density became larger, higher sensitivity was more beneficial for cooperation to evolve. Our study highlights that Lévy flights are an optimal searching strategy not only for foraging but also for constructing cooperative relationships with others.

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A schematic diagram of the n-player weightlifting game. In this game, players decide whether to cooperate or defect in carrying the weight. Cooperators need to pay a cost. The weightlifting can either succeed or fail. In case of success, all players receive a benefit. In case of failure, all players receive nothing. The player's pay-off depends on the benefit, cost and probability of success. Each player decides whether to cooperate or defect so as to maximize the expected gain.
Equilibria and optimal strategies of the four-player weightlifting game. Nash equilibria (a1,b1,c1,d1,e1) and Pareto optimal strategies (a2,b2,c2,d2,e2). (a1,a2) μ=10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =10$$\end{document}. (b1,b2) μ=20\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =20$$\end{document}. (c1,c2) μ=30\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =30$$\end{document}. (d1,d2) μ=40\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =40$$\end{document}. (e1,e2) μ=50\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =50$$\end{document}. The parameter regions for Nash equilibria and Pareto optimal strategies are as hatched in the i-c/b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c/b$$\end{document} plane, where i is the number of cooperators and c/b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c/b$$\end{document} is the cost-to-benefit ratio. We set σ=50\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma =50$$\end{document} in all cases. All players cooperate for a small value of c/b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c/b$$\end{document} (CT), while they defect for a large value (DT).
Equilibria and optimal strategies of the four-player weightlifting game. Nash equilibria (a1,b1,c1,d1,e1) and Pareto optimal strategies (a2,b2,c2,d2,e2). (a1,a2) μ=60\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =60$$\end{document}. (b1,b2) μ=70\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =70$$\end{document}. (c1, c2) μ=80\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =80$$\end{document}. (d1, d2) μ=90\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =90$$\end{document}. (e1, e2) μ=100\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu =100$$\end{document}. See Fig. 2 and the text for details.
Optimal strategies and cost-benefit analysis of the nn{\boldsymbol{n}}-player weightlifting game

May 2022

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162 Reads

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4 Citations

Diane Carmeliza N. Cuaresma

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Erika Chiba

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The study of cooperation has been extensively studied in game theory. Especially, two-player two-strategy games have been categorized according to their equilibrium strategies and fully analysed. Recently, a grand unified game covering all types of two-player two-strategy games, i.e., the weightlifting game, was proposed. In the present study, we extend this two-player weightlifting game into an nn-player game. We investigate the conditions for pure strategy Nash equilibria and for Pareto optimal strategies, expressed in terms of the success probability and benefit-to-cost ratio of the weightlifting game. We also present a general characterization of nn-player games in terms of the proposed game. In terms of a concrete example, we present diagrams showing how the game category varies depending on the benefit-to-cost ratio. As a general rule, cooperation becomes difficult to achieve as group size increases because the success probability of weightlifting saturates towards unity. The present study provides insights into achieving behavioural cooperation in a large group by means of a cost–benefit analysis.


How L\'evy flights triggered by presence of defectors affect evolution of cooperation in spatial games

May 2021

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26 Reads

Cooperation among individuals has been key to sustaining societies. However, natural selection favors defection over cooperation. Cooperation can be favored when the mobility of individuals allows cooperators to form a cluster (or group). Mobility patterns of animals sometimes follow a L\'evy flight. A L\'evy flight is a kind of random walk but it is composed of many small movements with a few big movements. Here, we developed an agent-based model in a square lattice where agents perform L\'evy flights depending on the fraction of neighboring defectors. For comparison, we also tested normal-type movements implemented by a uniform distribution. We focus on how the sensitivity to defectors when performing L\'evy flights promotes the evolution of cooperation. Results of evolutionary simulations showed that L\'evy flights outperformed normal movements for cooperation in all sensitivities. In L\'evy flights, cooperation was most promoted when the sensitivity to defectors was moderate. Finally, as the population density became larger, higher sensitivity was more beneficial for cooperation to evolve.


The weight-lifting game. (a) Two players lift the baggage (weight). A cooperator (C, white) pays a cost c, while a defector (D, black) does not. Each player receives either a reward b or nothing depending on whether the lifting is successful. The success probability pnc depends on the number of cooperators (nc = 0, 1 and 2). (b) We define Δp1 and Δp2 as the differences p1 − p0 and p2 − p1, respectively. Each of Δp1, Δp2 and Δp1 + Δp2 takes a numeric value between 0 and 1. (c) The payoff matrix of the weight-lifting game.
The success probability p(E, nc). (a) p(E, nc) is plotted against nc for E = 0, 0.25, 0.5, 0.75 and 1 (δ = 1/3). (b) pnc=p(E, nc) for nc = 0, 1 and 2 are shown on a line of unit length for E = 0, 0.25, 0.5, 0.75 and 1 (δ = 1/3).
Trajectory in the game phase diagram as the environmental value E varies from 0 to 1. (a) c/b = 1/2 and δ = 1/3. (b) c/b = 1/3 and δ = 1/3. (c) c/b = 1/2 (solid) and 1/3 (dashed) for δ = 1/3. (d) c/b = 1/2 and δ = 1/5. (e) c/b = 1/3 and δ = 1/5. (f) c/b = 1/2 (solid) and 1/3 (dashed) for δ = 1/5. The coloured areas represent all kinds of pairwise games, i.e. the prisoner's dilemma (PD: blue), the chicken game (CH: green), the stag hunt game (SH: red), D-dominant trivial (DT: purple) and C-dominant trivial (CT: yellow).
Change in game structure as the environmental value E varies. (a) Trajectories in the E–c/b diagram. (b) Trajectories in the E–b/c diagram. b/c = 3/(β − E)² for β = 1.5 (blue) and 1.3 (orange) (δ = 1/5). (c) How the game varies as the environmental value E changes from 0 to 1. The coloured areas represent all kinds of pairwise games, i.e. the prisoner's dilemma (PD: blue), the chicken game (CH: green), the stag hunt game (SH: red), D-dominant trivial (DT: purple) and C-dominant trivial (CT: yellow).
Improving environment drives dynamical change in social game structure

May 2021

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99 Reads

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1 Citation

The development of cooperation in human societies is a major unsolved problem in biological and social sciences. Extensive studies in game theory have shown that cooperative behaviour can evolve only under very limited conditions or with additional complexities, such as spatial structure. Non-trivial two-person games are categorized into three types of games, namely, the prisoner's dilemma game, the chicken game and the stag hunt game. Recently, the weight-lifting game has been shown to cover all five games depending on the success probability of weight lifting, which include the above three games and two trivial cases (all cooperation and all defection; conventionally not distinguished as separate classes). Here, we introduce the concept of the environmental value of a society. Cultural development and deterioration are represented by changes in this probability. We discuss cultural evolution in human societies and the biological communities of living systems.


Citations (3)


... We start by examining coevolutionary dynamics on 2D lattices, a topology that was first investigated in the context of evolutionary games by Nowak and May [4], as these structures can be easily represented as players on top of a surface, allowing us to easily visualize and investigate how spatial correlations affect the emergence of collective behavior [31,[56][57][58][59]. Initially proposed by Watts and Strogatz [60], the linkrewiring mechanism systematically alters network structure going from a well-ordered periodic structure (lattice) to a disordered random structure displaying the so-called "smallworld" phenomenon, where all agents in the system are at most few steps far apart from one another. ...

Reference:

Evolutionary game selection creates cooperative environments
How Lévy Flights Triggered by the Presence of Defectors Affect Evolution of Cooperation in Spatial Games

Artificial Life

... The pay-off of the cooperators is bp i − c , and the pay-off of the defectors is bp i ( Table 2). In terms of the parameters p 1 = p 1 − p 0 and p 2 = p 2 − p 1 , which represents the increase in the probability of success due to an additional cooperator, the following inequalities are obtained for the pay-offs R, T, S , and P (Table 1): 1 studied the evolution of cooperation in society by incorporating environmental value in the weightlifting game. They found that the evolution of cooperation seems to follow a DT to DT trajectory, which can explain the rise and fall of human societies. ...

Improving environment drives dynamical change in social game structure

... The neighbors also play the game with their neighbors and obtain payoffs. (Miyagawa et al., 2020). Here, we extended it so that the intensity of Lévy flights can be adjusted. ...

How Lévy Flights Triggered by Presence of Defectors Affect Evolution of Cooperation in Spatial Games