Towards fine grained human behaviour simulation models

Agent based simulation modelers have found it difficult to build grounded fine grained simulation models of human behavior. By grounded we mean that the model elements must rest on valid observations of the real world, by fine grained we mean the ability to factor in multiple dimensions of behavior such as personality, affect and stress. In this paper, we present a set of guidelines to build such models that use fragments of behavior mined from past literature in the social sciences as well as behavioral studies conducted in the field. The behavior fragments serve as the building blocks to compose grounded fine grained behavior models. The models can be used in simulations for studying the dynamics of any set of behavioral dimensions in some situation of interest. These guidelines are a result of our experience with creating a fine grained simulation model of a support services organization.

[1]  Meghendra Singh,et al.  An agent based exploration of a relationship between daily routines and convenience store footfalls , 2015, SummerSim.

[2]  Barry G. Silverman,et al.  NonKin Village: An Embeddable Training Game Generator for Learning Cultural Terrain and Sustainable Counter-Insurgent Operations , 2009, AGS.

[3]  T. Schelling Models of Segregation , 1969 .

[4]  Joshua M. Epstein,et al.  Learning to Be Thoughtless: Social Norms and Individual Computation , 2001 .

[5]  Barry G. Silverman,et al.  Rich socio-cognitive agents for immersive training environments: case of NonKin Village , 2011, Autonomous Agents and Multi-Agent Systems.

[6]  C. Lebiere,et al.  The Atomic Components of Thought , 1998 .

[7]  James Ness,et al.  The Science and Simulation of Human Performance , 2004 .

[8]  Eva Hudlicka Modeling Effects of Behavior Moderators on Performance: Evaluation of the MAMID Methodology and Architecture , 2015 .

[9]  A. Bakker,et al.  Job demands, job resources, and their relationship with burnout and engagement: a multi‐sample study , 2004 .

[10]  David Watson,et al.  The PANAS-X manual for the positive and negative affect schedule , 1994 .

[11]  M EpsteinJoshua Learning to Be Thoughtless , 2001 .

[12]  R. Axelrod The Dissemination of Culture , 1997 .

[13]  W. Hamilton,et al.  The evolution of cooperation. , 1984, Science.

[14]  Michael Luck,et al.  Establishing Norms for Network Topologies , 2011, COIN@AAMAS&WI-IAT.

[15]  Thomas C. Schelling,et al.  Dynamic models of segregation , 1971 .

[16]  Joshua M. Epstein,et al.  Modelling to contain pandemics , 2009, Nature.

[17]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[18]  David Clarance,et al.  Norm Establishment in a Single Dimension Axelrod Model , 2015, PRIMA.

[19]  Chockalingam Viswesvaran,et al.  Personality and absenteeism: a meta‐analysis of integrity tests , 2003 .

[20]  A. Ryan,et al.  Explaining the Links between Workload, Distress, and Work-Family Conflict among School Employees: Physical, Cognitive, and Emotional Fatigue. , 2015 .

[21]  Kay W. Axhausen,et al.  An Agent-Based Microsimulation Model of Swiss Travel: First Results , 2003 .

[22]  I. Janis,et al.  Decision Making: A Psychological Analysis of Conflict, Choice, and Commitment , 1977 .

[23]  Barry G. Silverman,et al.  Toward Realism in Human Performance Simulation , 2004 .

[24]  Ramon J. Aldag,et al.  Decision Making: A Psychological Analysis of Conflict , 1980 .

[25]  Peter Hidas,et al.  MODELLING LANE CHANGING AND MERGING IN MICROSCOPIC TRAFFIC SIMULATION , 2002 .

[26]  R. Yerkes,et al.  The relation of strength of stimulus to rapidity of habit‐formation , 1908 .