WSC 2007 Final Abstracts

The Range of Predictions for Calibrated Agent-based Simulation Models
DongFang Shi and Roger J. Brooks (Lancaster University)

Abstract:
Agent-based simulation is increasingly used to study systems in many areas of business and science. Using agent-based simulation for prediction could be very valuable. However, these models usually have a lot of parameters which are difficult to measure directly leading to uncertainty as to the best values to use. Obtaining the values for the parameters may require calibration of the model against observed historical output data. This type of problem is an inverse problem and there may be many sets of feasible parameter values giving a wide range of predictions. The work described here investigated the extent of this problem for a word of mouth consumer model.

Upgraded Cellular Automata Based Group-work Interaction Simulation
Dong Shengping and Hu Bin (Huazhong University of Science and Technology)

Abstract:
The simulation of group-work interaction is significant for Chinese enterprise organizational management. As a result, a cellular-automata based simulation model is put forward. The number of cells is specified equal to the one of group members. Group members consist of working-hard members and social ones, or regular ones and irregular ones. Time delay and information distortion are taken into account in the model. Work includes various degrees of hard and soft work. The model is coded into Group-Work Interaction System by Visual Basic 6.0. The validation of the system is conducted by choosing the group of adjustable parameters to achieve the optimal match of group members with their work resulting in good group behavior and high work efficiency. Many rules and related phenomena are discussed and analyzed in validation, as well as the implication of the system and further works are offered in the end.

Spatial Emergence of Genotypical Tribes in an Animat Simulation Model
Ken A. Hawick, Chris J. Scogings, and Heath A. James (Massey University)

Abstract:
We observe the spontaneous emergence of spatial tribes in an animat agent model where simple genetic inheritance is supported. Our predator-prey model simulates a flat-world of animat agents which breed, move, eat and predate according to priorities encoded in their genotype. Initialising a random mixture of all possible priority list genotypes, we find not only that only a small fraction of possible genotypes are favoured for survival, but that distinct spatial patterns of different tribes emerge. We report on the emergent macroscopic features in our model and discuss their correspondent mapping to microscopic animat rules and genotypes. Even a simple gene-reordering mechanism gives rise to complex emergent behaviour.

Agent-model Validation Based on Historical Data
Lance E. Champagne (JDICE) and Raymond R. Hill (Wright State University)

Abstract:
Combat, unlike many real-world processes, tends to be singular in nature. That is, there are not multiple occurrences from which to hypothesize a probability distribution model of the real world system. Mission-level models may offer more flexibility on some measures due to their extended time frame. Additionally, the parameters involved in the mission-level model may be unchanged for significant stretches of the total simulation time. In these cases, time periods may be devised so that the periods hold sufficiently similar traits such that the incremental results may be assumed to come from a common distribution. This paper details a new statistical methodology for use in validating an agent-based mission-level model. The test is developed within the context of the Bay of Biscay agent-based simulation and uses the monthly data from the extended campaign as a basis of comparison to the simulation output.

An Exploration-based Taxonomy for Emergent Behavior Analysis in Simulations
Ross Gore, Paul F. Reynolds and Jr. (University of Virginia)

Abstract:
Emergent behaviors in simulations require explanation, so that valid behaviors can be separated from design or coding errors. We present a taxonomy, to be applied to emergent behaviors of unknown validity. Our goal is to facilitate the explanation process. Once a user identifies an emergent behavior as a certain type within our taxonomy, exploration can commence in a manner befitting that type. Exploration based on type supports narrowing of possibilities and suggests exploration methods, thus facilitating the exploration process. Ideally, a taxonomy would be robust, allowing reasonable variation in behavior type assignment without penalty in cost or correctness during the exploration process. The taxonomy we present is robust, comprehensive and suitable for use with our established emergent behavior exploration methods. In addition to the taxonomy, we present our design rationale, and a summary of results from a test application of our taxonomy.

Modeling Organizational Adaptation: A Replication of Levinthal's Model of Emergent Order
Brian F. Tivnan (The MITRE Corporation)

Abstract:
Levinthal's application of Kauffman's NK model to economic firms continues to be one of the most accepted computational models in organization science. Levinthal investigates the impact to organizational fitness from both adaptive search and the interactions of strategic components within an organization. Despite concerns regarding the applicability of Kauffman's NK model to organization science, Levinthal's initial study has received limited critical analysis and has not been independently replicated. Building on previous replication research of Tivnan, this paper describes the formulation, successful replication and critical analysis of Levinthal's model of emergent order in contribution towards a model-centered organization science. The paper concludes with a discussion of a credibility assessment of the replication results; namely, model verification and validation.