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Complex Network Theory and Agent-Based Modeling
Takao TERANO 한국경영과학회 2016 한국경영과학회 학술대회논문집 Vol.2016 No.10
In this talk, I will present a new model of micro-macro social learning model for a classical economic problem “the emergence of money”. We propose Doubly Structural Network (DSN) Model, which consists of one global social network of agents and internal networks that represent agents’ recognition. DSN model enables us to describe the emergence of proto-money as a self-organization process of the common recognition of exchangeability. We conduct an analytical method and a numerical approach into bifurcation phenomena of a new mean-field dynamics derived from this DSN Model. The main contribution of the paper is summarized as follows. (1) The proto-money can emerge from commodities without distinctive properties. (2) The social network degree is a definitive factor for non-/single-/multiple-emergence of proto-money. (3) The variance of the social network degree (existence of hubagents) also affects emergence of proto-money.
Khan Muhammad Badruddin,Takashi Yamada,Takao Terano 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In this paper, we present the simulation of a new variant of Snakes and Ladders Board Game in which the player is allowed either to avail or avoid the opportunity to jump to top of the ladder, when the roll of die leads to foot of the ladder, for some strategic reasons. The outcome of the change in the game results in emergence of scope for various new strategies for players to acquire besides the conventional “"always-jump”" approach. This paper describes the results of the simulation of the game, when played with four different strategies. The results show that it can be useful for a player on some occasions to avoid the jumping opportunity in order to increase the chance of winning the game.
Chao YANG,Toru TAKAHASHI,Takashi YAMADA,Setsuya KURAHASHI,Isao ONO,Takao TERANO 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Pattern Oriented Modeling (POM) is an approach to bottom-up complex system analysis which was developed in ecology and for agent-based complex systems. This paper proposes a pattern-oriented agent-based simulation (POABS) approach to analyze agent-based complex system. We apply POABS to a history simulation domain to analyze a particular family line with more successful candidates in the civil service examination in imperial China. In order to decode family strategies along such an elite family line, we develop POABS to test relevant patterns observed in the real family system. Inverse simulation technique is applied to evaluate each simulated pattern through fitting the time-series of simulated profile data to real profile data with real-coded GA. Intensive experiments show a practical applicability of POABS in agent-based complex system.