Artificial Life Simulation of the Textile / Apparel Marketplace : An Innovative Approach to Strategizing About Evolving Markets

A-Life techniques allow researchers to build rigorous, replicable simulations of nonequilibrium systems. The simulations are laboratories where the behavior of adaptive agents can be observed across time, rule sets can be recalibrated to investigate different scenarios, and emerging patterns of behavior can be identified and interpreted. The aim of A-Life simulation, like all computer modeling, is to link the micro behavior of the simulated system with the macro behavior of society. In this project, the textile/apparel marketplace is viewed as a complex, adaptive, self-organizing system--that is, the actions of agents (consumers, firms, etc.) create a system in rolling equilibrium. Through the agents’mutual accommodation and mutual rivalry, new structures and behavior patterns emerge continuously. Two simulation prototypes-SUMERS, a simulation of diffusion of innovation, and SPHERE OF INFLUENCE, a simulation of consumer/supplier interactions--have been designed and programmed. The process of simulation development includes stages for design and programming, verification, validation, and sensitivity analysis. A network of simulation developers, textile/apparel scholars, and industry executives assist the core team in developing A-Life simulations for theory building, generating hypotheses for empirical research, strategizing about evolving markets, and conducting computer experiments. National Textile Center Annual Report: November 1996 31 A95-2012 A-Life Simulation as a Method of Inquiry Theorists and researchers (Levy, 1992; Waldrop, 1992; Holland, 1995) working in the field of self-organizing systems are forging an alternative to the traditional linear, reductionist approach to inquiry. The new paradigm is based on a basic insight: a system of agents following simple rules can exhibit complex behaviors. Only recently have tools like graphic workstations made it possible to study adaptive agents through simulation. As a new method of inquiry, A-Life (Langton, 1989) techniques allow researchers to build rigorous, replicable computer-based experiments of non-equilibrium systems. In this new science, computer simulation acts as both theory and experiment. Developing simulations requires deep understanding of the inputs, outputs, and behaviors within a system--a theory of the system. For complex, self-organizing systems, the simulation is a laboratory where agents’actions can be observed across time, rule sets can be recalibrated to investigate different scenarios, and emerging patterns of behavior can be identified and interpreted. Unlike other modeling approaches, no agent operates with perfect global knowledge. Instead agents in A-Life simulations operate in their world with incomplete knowledge using heuristics rather than pure rationality, much like consumers and firms in the real world. At each time step, each agent assesses its own condition in comparison to its preference set, evaluates the environment within its sight range, and determines its behavior in the next time step. There is no coordinating authority to dictate the system’s behavior. Rather the collective behavior of all agents determines the pattern and environment at each successive time step. The method is particularly valuable for phenomena where the actions occur over a long time span, the system is inaccessible for observation, or the dynamics of a system are subtle. This method of inquiry can even be used to recreate an historical system and play what-if games involving alternative scenarios. The aim of A-Life simulation, like all computer modeling, is to link the micro behavior of the simulated system with the macro behavior of society. An investigator using A-Life simulation begins with a theory of the system gleaned from observation and previous research. The theory is translated into precise specification of system parameters, agent variables, rule sets, and timetables and programmed in the simulation software. Programming is not a trivial process because decisions about the how to display data, the stages of initializing the simulation, and selection of a suitable pseudo-random number generator can effect simulation validity. Once a prototype of the simulation is complete, the next stage is verification and validation. In verifying the simulation, investigators run trials of the model exploring combinations of parameters and variables to determine if the theory of the system has been captured in the simulation design and that the internal logic of the model is operating in a consistent and reasonable way. Verification may also include evaluation of the model by experts in the field. In the validation stage, results from the model are matched against existing data. Problems at this stage lead to revision of the system specifications. If the purpose of the project is exploratory--for theory building, generating hypotheses for empirical testing, or strategizing--this level of development may be sufficient to produce a useful 32 National Textile Center Annual Report: November 1996