Shingo Takahashi

On conditions for a meeting not to reach a deadlock

A formal model for describing the interaction stage in a meeting, particularly, persuasion and compromise by the members in a meeting, is proposed. Using the model, a sufficient condition for a meeting not to reach a deadlock is provided. The condition implies that stable emotion of members of a meeting is essential for the meeting not to reach a deadlock.

Landscape Analysis of Possible Outcomes

The behavior of a complex social system is unpredictable because both the uncertainties and the complex interactions in the system affect its future behavior. Existing scenario analysis methods focus on the effects of complex interactions of the system upon the system’s behavior, rather than the uncertainties in the system. The purpose of this paper is to develop a novel scenario analysis method that mainly focuses on evaluating a range of possible outcomes in a system based on selected uncertainties. We validate this method by applying it to a case example in which the configuration of an evaluation system for a sales division is examined.

Evolutionary approach to three-person hypergame situation

Presents an evolutionary approach to the hypergame situation based on a three-person hypergame. Our evolutionary approach could primarily deal with the learning problem in a hypergame situation modeled as a network-type dynamic hypergame (NTDH game), which describes a social situation in which each agent perceives the situation autonomously and makes his decision solely based on an individually dependent model. In the paper an NTDH game is simulated by applying a genetic algorithm. Then the essential types of information be required to improve each agents perception of the situation are considered.

An analysis of an incentive problem considering non‐monetary utility

The present paper describes a new approach to the agency model, which is a cybernetic model designed to analyze the situation in which an economic actor (the principal) controls the behavior of another actor (the agent), by including the factor of non‐monetary utility (intrinsic motivation). The new model addresses an incentive problem in an organization. An organization is considered to be a system constituted by the principal and the agent. Two state variables of this system, namely productivity and intrinsic motivation, are the focus of the present study. The effect of these variables on how to offer incentive is analyzed. The following results were obtained: when productivity is high and the strength of intrinsic motivation and uncertainty of output are low, organizations should introduce performance‐based incentive system.

General morphism for modeling relations in multimodeling

by A. ADAMS P. FRIEDMAN K. SAKATA University of British Columbia ARTHUR ADAMS was born in Ottawa, Canada in 1943. He completed his early schooling there and then moved with his family to Whitehorse, Yukon, Canada. He lived as a student for two years in Victoria, then transferred to the University of British Columbia in Vancouver where he was awarded his BASC in Chemical Engineering in 1967. He is now working toward an MASc in Chemical Engineering at the University of British Columbia. His summer employment has ranged from survey and road construction work to maintenance and engineering in chlorine and caustic plants, as well as research conducted by the British Columbia Research Council into air pollution from pulp mills. His present thesis work is concerned with studies in heat transfer among immiscible liquids during vaporization, a practical application being the desalination of sea water.

Why Stratification of Networks Emerges in Innovative Society

In this chapter, we model an innovative society like Silicon Valley, California, in terms of a polyagent system, and then apply to it the coordination management framework and the interaction prediction principles introduced in Chap. 1. It is then shown that it is very natural for such a society to produce stratification of networks for intelligent entrepreneurs to cope with the complexity around them. Some of the main theoretical contributions of this research concern how the interaction prediction principle works with subjective game situations described by the polyagent system.

Simulation approach to learning problem in hypergame situation by genetic algorithm

This paper presents a simulation approach to adaptation process of two interacting parties (or groups), each of which adopts learning behavior in hypergame situation. That is, we try to clarify which learning behavior facilitates the adaptation process to converge on equilibria of the traditional game situation (TGS), and facilitates each agent to learn the equilibria correctly. First, we define the hypergame situation, in which each agent is assumed to have only internal model of the situation. Then, we develop adaptation process model of the groups, and a simulation of the process. In the model, the genetic algorithm has role to improve population of perceptions according to the past experiences. Finally, we point out that by examining the simulation results, action choice and perception evaluation based on subjective Nash equilibria are critical to the performance of the adaptation process, in the situations with one or more TGS Nash equilibria.

Method for Getting Parameters of Agent-Based Modeling Using Bayesian Network: A Case of Medical Insurance Market

To date, agent-based social simulation (ABSS) is a popular method to study the behavior of a social system and the interaction of the constituent members of the system. With the development of computer and information technologies, many ABSS approaches have been proposed with wide application. However, the definitive methodology for modeling of the agent’s behavior in ABSS has not been established yet. This study proposes a new methodology of modeling of the agent’s behavior in ABSS using Bayesian network based on the questionnaire survey. This method enables us to simultaneously perform the construction of the agent’s behavior model and the estimation of the internal parameters within the model. This study took a Japanese medical insurance market as an example, since this complicated market deserves detailed consideration. We verified the effectiveness of the proposed methodology by applying the scenario analysis to this case.

Hybrid Approach of Agent-Based and Gaming Simulations for Stakeholder Accreditation

We propose a hybrid approach of agent-based and gaming simulations for stakeholder accreditation. This approach assumes greater uncertainties about an agent’s decision-making and learning processes, severe time constraints on stakeholder participation in the modeling and simulation process, and limited understanding of an agent-based modeling language by stakeholders. The approach transforms an agent-based model into a game with adequate similarities between them, and it allows stakeholders to understand an agent-based model by playing the game. We developed a transformation-modeling protocol to convert an agent-based model into a valid and playable card game, and we introduce an example in which an agent-based model is transformed to assist the design of a performance measurement system in a sales organization. We report preliminary results for our approach.

Agent-based simulation of adaptive organizational structures to environmental change

This paper proposes an agent-based simulation model for analyzing adaptive processes of organizational structures to environmental changes. The model is based on a framework of computational organization theory (COT) and performed using the method of inverse simulation with genetic algorithm. The model specifies an activity process in organization that is composed of tasks, agents and environments. The roles of agents: normal and leader, are defined based on some basic tasks of agents: recognizing tasks from environment, sharing knowledge required for processing tasks, and coordinating tasks among agents. Organizational structures are distinguished according to the configuration of the roles of agents resolving the tasks recognized. Adaptation process is represented as learning process of agents’ internal models of environments. Environments are expressed as task generators and fall into 5 types. This paper shows some fundamental relationships between organizational structures and dynamically changing environments.