Numerical Study of Game Theory

Similar documents
Game Theory, Population Dynamics, Social Aggregation. Daniele Vilone (CSDC - Firenze) Namur

arxiv: v1 [physics.soc-ph] 27 May 2016

Phase transitions in social networks

Understanding and Solving Societal Problems with Modeling and Simulation

Evolutionary Game Theory

arxiv: v2 [q-bio.pe] 18 Dec 2007

Belief-based Learning

Problems on Evolutionary dynamics

Evolutionary prisoner s dilemma game on hierarchical lattices

Game Theory. Professor Peter Cramton Economics 300

Spatial three-player prisoners dilemma

Collective Evolution of Turn-taking Norm in Dispersion Games

Bounded Rationality, Strategy Simplification, and Equilibrium

Phase Transition of Finance Chia Cheng Chang

Research Article Snowdrift Game on Topologically Alterable Complex Networks

arxiv: v3 [cs.gt] 25 Feb 2012

Damon Centola.

Scale-invariant behavior in a spatial game of prisoners dilemma

Evolution & Learning in Games

Optimal Decision Rules in Repeated Games Where Players Infer an Opponent s Mind via Simplified Belief Calculation

Game Theory and Algorithms Lecture 2: Nash Equilibria and Examples

Evolution of Cooperation in Continuous Prisoner s Dilemma Games on Barabasi Albert Networks with Degree-Dependent Guilt Mechanism

The Stability for Tit-for-Tat

Umeå University. Access to the published version may require subscription.

Outline. 3. Implementation. 1. Introduction. 2. Algorithm

Reciprocity in evolving social networks

VII. Cooperation & Competition

Diversity-optimized cooperation on complex networks

Graph topology and the evolution of cooperation

arxiv: v1 [quant-ph] 30 Dec 2012

Prisoner s Dilemma. Veronica Ciocanel. February 25, 2013

EVOLUTIONARY STABILITY FOR TWO-STAGE HAWK-DOVE GAMES

Evolution of public cooperation on interdependent networks: The impact of biased utility functions

Evolutionary dynamics of cooperation in neutral populations

Evolutionary games on complex network

Behavior of Collective Cooperation Yielded by Two Update Rules in Social Dilemmas: Combining Fermi and Moran Rules

Understanding Emergence of Cooperation using tools from Thermodynamics

Meaning, Evolution and the Structure of Society

Herd Behavior and Phase Transition in Financial Market

CS 798: Multiagent Systems

Group Formation: Fragmentation Transitions in Network Coevolution Dynamics

A Dynamic Level-k Model in Games

Evolution of a social network: The role of cultural diversity

Minimal-Agent Control of Evolutionary Games on Tree Networks

Communities and Populations

6 The Principle of Optimality

UC Berkeley Haas School of Business Game Theory (EMBA 296 & EWMBA 211) Summer 2016

Quantum Games. Quantum Strategies in Classical Games. Presented by Yaniv Carmeli

arxiv: v2 [physics.soc-ph] 29 Aug 2017

Game Theory -- Lecture 4. Patrick Loiseau EURECOM Fall 2016

Game Theory for Linguists

Interdependent network reciprocity in evolutionary games

The Evolution of Cooperation in Self-Interested Agent Societies: A Critical Study

Evolution of Cooperation in Evolutionary Games for Heterogeneous Interactions

Pareto-Improving Congestion Pricing on General Transportation Networks

Evolutionary prisoner s dilemma games coevolving on adaptive networks

Emotional Agents at the Square Lattice

A (Brief) Introduction to Game Theory

Quantum Bertrand duopoly of incomplete information

arxiv:math/ v1 [math.oc] 29 Jun 2004

Evolution of Cooperation in the Snowdrift Game with Incomplete Information and Heterogeneous Population

Asymmetric cost in snowdrift game on scale-free networks

SONDERFORSCHUNGSBEREICH 504

Cooperation Stimulation in Cooperative Communications: An Indirect Reciprocity Game

Iterated Strict Dominance in Pure Strategies

Repeated Games and Direct Reciprocity Under Active Linking

arxiv:physics/ v1 [physics.soc-ph] 9 Jun 2006

Analytical investigation on the minimum traffic delay at a two-phase. intersection considering the dynamical evolution process of queues

Introduction to Game Theory. Outline. Topics. Recall how we model rationality. Notes. Notes. Notes. Notes. Tyler Moore.

arxiv:cond-mat/ v1 [cond-mat.other] 4 Aug 2004

Time-Series Based Prediction of Dynamical Systems and Complex. Networks

The Cross Entropy Method for the N-Persons Iterated Prisoner s Dilemma

Writing Game Theory in L A TEX

Basic Game Theory. Kate Larson. January 7, University of Waterloo. Kate Larson. What is Game Theory? Normal Form Games. Computing Equilibria

Evolutionary Games on Networks. Wen-Xu Wang and G Ron Chen Center for Chaos and Complex Networks

Observations on Cooperation

Emergence of Cooperation and Evolutionary Stability in Finite Populations

Some Thought-ettes on Artificial Agent Modeling and Its Uses

Introduction to Game Theory

A lattice traffic model with consideration of preceding mixture traffic information

Game Theory, Evolutionary Dynamics, and Multi-Agent Learning. Prof. Nicola Gatti

Computational Problems Related to Graph Structures in Evolution

Opinion Formation and Social Systems

Modeling Dynamic Evolution of Online Friendship Network

Topology-independent impact of noise on cooperation in spatial public goods games

Internalization. a Game-Theoretic Approach. Rolf Ziegler Institute of Sociology University of Munich

ADAPTIVE GROWING NETWORKS COEVOLVING WITH THE SPREAD OF DISEASES

Vector fields and phase flows in the plane. Geometric and algebraic properties of linear systems. Existence, uniqueness, and continuity

Evolutionary Bargaining Strategies

Lectures Road Map

Static (or Simultaneous- Move) Games of Complete Information

Computing Minmax; Dominance

arxiv: v1 [physics.soc-ph] 9 Jan 2010

Evolutionary Game Theory and Frequency Dependent Selection

Globalization-polarization transition, cultural drift, co-evolution and group formation

arxiv: v1 [cs.gt] 18 Dec 2017

An Interruption in the Highway: New Approach to Modeling the Car-Traffic

Evolutionary Games and Computer Simulations

Approximate Nash Equilibria with Near Optimal Social Welfare

A Folk Theorem For Stochastic Games With Finite Horizon

Transcription:

New Physics: Sae Mulli (The Korean Physical Society), Volume 60, Number 9, 2010 9, pp. 943 951 DOI: 10.3938/NPSM.60.943 Integrated Science Laboratory, Department of Physics, Umeå University, 901 87 Umeå, Sweden, 440-746 (2010 9 1, 2010 9 15 ).,, (sociophysics).. (Intelligent Tit-for-tat) כ.,.. :,,,,,, Numerical Study of Game Theory Seung Ki Baek Integrated Science Laboratory, Department of Physics, Umeå University, 901 87 Umeå, Sweden Beom Jun Kim Department of Physics, Sungkyunkwan University, Suwon 440-746 (Received 1 September, 2010 : accepted 15 September, 2010) A game-theoretic approach has been providing a powerful tool in the qualitative understanding of macroscopic social phenomena in social sciences, e.g., in economics and in political science. Recently, researchers in physics, especially in statistical physics, have also used these game-theoretic approaches, but in a more quantitative way, and have been producing a variety of interesting results in the new research area called sociophysics by studying human society as a complex system. This work introduces recent works that have tackled the combinatorial complexities arising in gametheoretic studies with the aid of simplified assumptions and numerical computations. We first show how cooperation emerges in the prisoner s dilemma game when each player s memory capacity is enhanced and suggest that the intelligent tit-for-tat strategy plays a crucial role in the history of -943-

-944-, Volume 60, Number 9, 2010 9 cooperation. Then, we numerically show that there is a certain case of simultaneous coordination among many players where the system has a high risk of failure when everyone is willing to follow the coordination, which is actually higher than when some are not concerned about it. Lastly, we discuss a mathematical treatment of an equilibrium solution for a reverse auction game, which is a variant of the minority game, and its computational approach. PACS numbers: 89.75.-k, 87.23.Ge Keywords: Game theory, Sociophysics, Prisoners dilemma game, Tit-for-tat strategy, Pedestrian problem, Reverse auction game, Equilibrium point I. (sociophysics), [1]., [2], [3], [4], [5], [6], [7].,.,.. כ,. כ (coordination game). כ, כ (strategy)..,,.,. כ E-mail: beomjun@skku.edu.., [8], [9]. [10]. II..., כ 4.,.. כ. 4. 4, כ., כ., כ כ, כ

-945- כ., (4 )., כ, כ כ... (, cooperation C ) (, defection D ) כ,.. (Nash equilibrium, ). כ.. (, )=(C, C), (C, D), (D, C), (D, D)., 5. C D 2 5 = 32. 5 b כ : b 0 b(c, C), b(c, D), b(d, C), b(d, D). b 0 (= C D) C D. C C CCCC. C CDDD (Grim Trigger, GT) C CDCD (Tit-for-tat, TFT). C CDDC (b(c, C) = C, b(d, C) = D), (b(c, D) = D, b(d, D) = C), Fig. 1. Numerical integration results of Eq. (1). (Pavlov)., 90 % C 10 % D. (C, C) 3, (C, D) 5, (D, C) 0, (D, D) 1. i p i (i = 1,, 32)., כ. כ (replicator dynamics, RD) כ. dp i dt = (U i U )p i. (1) U i i U ( j U jp j ). 32 RD (Fig. 1).., 4, GT, TFT, Pavlov C CDCC. C CDXY XY 4 (X = C, D Y = C, D).,, כ. 28 XY

-946-, Volume 60, Number 9, 2010 9.,. 2. 1 q (q 1). (e = 0) RD כ 4 כ. כ, (1) כ. 0 < e 1, כ. C CDCC, GT, TFT Pavlov (cyclic dominance) TFT GT Pavlov TFT Pavlov GT. TFT GT Pavlov. GT כ Pavlov (D, D) C. TFT (C, D) (D, C). TFT Pavlov.,. (CC, CC) (DD, DD) 16 2 2 18 = 262, 144. 32 28 כ. RD.. כ כ. Fig. 2. Characteristic results of the iterated prisoner s dilemma game on the two-dimensional 128 128 square lattice with q = 0.05. The error probabilities are given as (a) e = 0 and (b) e = 0.01, respectively. כ. (1),., 32. 2 18 95 %, כ כ. כ. 5 % Pavlov. כ. 5%. (Efficient Cooperator, EC), Pavlov. EC

-947- Fig. 3. Change of fractions of three representative strategies belonging to EC, ET, and I-TFT, respectively, when simulated on the 32 32 square lattice. Fig. 4. Our model of a pedestrian.. GT (Efficient Trigger, ET). EC כ כ כ. Intelligent Tit-for-tat(I-TFT) כ, TFT. TFT. I-TFT כ. EC TFT כ. EC כ. I-TFT TFT,. כ. I-TFT EC Fig. 3. III. (1). (L) (R), (L,L) (R, R) 1 (R, L) (L, R) 0. p R R p L = 1 p R L (1) dp R dt = p R (1 p R )(2p R 1) (2) p R = 0, 1, 1/2. כ p R = p L = 1/2,. כ (1). כ...., q, 1 q.,,. כ כ ( )...

-948-, Volume 60, Number 9, 2010 9 Fig. 5. Occurrence of jamming observed in the pedestrian model... (q = 1)., כ. כ. (dead-lock)... t 1. (free flow) כ, (jamming) כ. כ (steady state). ρ φ,. Fig. 6 ρ φ 1 0, τ. φ 1 0 כ.. ρp q=1 ρ(1 p) q=1/2 כ. (ρ, p) φ, Fig. 7. Fig. 6. Results from the numerical simulations where the size of the road is set to be X Y = 50 200. Fig. 7. Average flow as a function of the pedestrian density and the fraction of rule followers. The road sizes are (a) 50 200 and (b) 100 400, respectively. כ, כ (p = 1). p = 1 (Fig. 8)... כ כ.., RD כ,

-949- Fig. 8. Typical configuration at t = 1000 when started with ρ = 0.2 and p = 1 on a road of 100 400. כ (transient phenomenon) כ. RD כ,. כ כ. IV.. (anti-coordination). כ כ כ. כ. כ. n 1 n. כ. 1 2 2 1. 1 n, i p i כ. p i = 1.? (evolutionarily stable strategy), כ כ. (neutrally stable strategy),,. n = 3. {p i }? 9 {π i }. (, )=(1,1), p 2 1. 2 3 1, π 2 + π 3. 9. כ π i p i כ. כ n 3. כ. n = 3 Z 0 = (p 1 + p 2 + p 3 ) 2 9 9. 1? 9 p 1 1 כ כ.. Z 0 p 1 p 1 0 כ., 1. Z 1 = Z 0 p 1 [dz 0 /dp 1 ](p 1 = 0) (3), 2 Z 1. Z 2 = Z 1 p 2 [dz 1 /dp 2 ](p 2 = 0) (4), i 1 i Z i 1 p i = 0. c i = [Z i 1 ](p i = 0) (5). W = c i π i (6) π i = 1 c i i {π i } W. c i כ n 1

-950-, Volume 60, Number 9, 2010 9 Fig. 10. Numerical results when players imitate successful strategies. The black dotted lines show the analytically obtained equilibrium solutions. Fig. 9. Equilibrium solutions of the reverse auction game. Both panels (a) and (b) depict the same data but panel (b) scales both axes in terms of n to compare the outcomes with the uniform solution. n 1 {p i }. Fig 9., (p i = π i ), p i = 1/n. n כ., כ. כ n. כ n = 12... n כ. כ. π i p i כ. p i n 1 כ.,. V.,,., -,,,,.,, כ. [1] C. Castellano, S. Fortunato and V. Loreto, Rev. Mod. Phys. 81, 591 (2009). [2] D. Helbing, I. Farkas and T. Vicsek, Nature 407, 487 (2000). [3] F. Vazquez and V. M. Eguiluz, New J. Phys. 10, 063011 (2008). [4] V.M. Eguiluz and M. G Zimmermann, Phys. Rev. Lett. 85, 5659 (2000). [5] D. Helbing, Rev. Mod. Phys. 73, 1067 (2001).

-951- [6] A.-L. Barabasi, Nature 435, 207 (2005); C. Song, Z. Qu, N. Blumm and A.-L. Barabasi, Science 327, 1018 (2009). [7] G. Szabo and G. Fath, Phys. Rep. 446, 97 (2007). [8] S.K. Baek and B.J. Kim, Phys. Rev. E 78, 011125 (2008). [9] S.K. Baek, P. Minnhagen, S. Bernhardsson, K. Choi and B. J. Kim, Phys. Rev. E 80, 016111 (2009). [10] S.K. Baek and S. Bernhardsson, Fluctuation Noise Lett. 9, 61 (2010).