Agent-based Simulation Approach Research Articles

The emergence of Zipf's Law in a system of cities: An agent-based simulation approach

This paper develops a spatial agent-based model to generate a system of cities that exhibits the statistical properties of the Zipf's Law. The numerical results suggest that the combination of bounded rationality and maximum heterogeneity of agents can produce a generic power-law relationship in the size distribution of cities, but does not always generate the Zipf's Law. We found sufficient conditions on the probability distribution of spatial reach to generate the Zipf's Law associated with unit power coefficient. Our model also indicates that the Zipf's Law breaks down unless the extent of agglomeration economies overwhelms the negative disagglomerating forces.

Complexity theory and collaboration: An agent-based simulator for a space mission design team

In this paper, we investigate how complexity theory can benefit collaboration by applying an agent-based computer simulation approach to a new form of synchronous real-time collaborative engineering design. Fieldwork was conducted with a space mission design team during their actual design sessions, to collect data on their group conversations, team interdependencies, and error monitoring and recovery practices. Based on the fieldwork analysis, an agent-based simulator was constructed. The simulation shows how error recovery and monitoring is affected by the number of small group, or sidebar, conversations, and consequent noise in the room environment. This simulation shows that it is possible to create a virtual environment with cooperating agents interacting in a dynamic environment. This simulation approach is useful for identifying the best scenarios and eliminating potential catastrophic combinations of parameters and values, where error recovery and workload in collaborative engineering design could be significantly impacted. This approach is also useful for defining strategies for integrating solutions into organizations.

Real-world market representation with agents

As power markets are relatively new and still continue to evolve, there is a growing need for advanced modeling approaches that simulate the behavior of electricity markets over time and how market participants may act and react to the changing economic, financial and regulatory environments in which they operate. A new and rather promising approach is to model the electricity market as a complex adaptive system using an agent-based modeling and simulation (ABMS) approach. The purpose of an ABMS model is not necessarily to predict the outcome of a system but to reveal and understand the complex and aggregate system behaviors that emerge from the interactions of the heterogeneous individual entities. Emergent behavior is a key feature of ABMS and is not easily inferred from the simple sum of the behavior of its components. By relying on both established engineering modeling techniques as well as advanced quantitative economic market principles, the ABMS approach is uniquely suited to addressing the strategic issues of interest to different market participants as well as those of market monitors and regulators.

Approaches to simulating the “March of Bricks and Mortar”

Re-creation of the extent of urban land use at different periods in time is valuable for examining how cities grow and how policy changes influence urban dynamics. To date, there has been little focus on the modeling of historical urban extent (other than for ancient cities). Instead, current modeling research has emphasized simulating the cities of the future. Predictive models can provide insights into urban growth processes and are valuable for land-use and urban planners, yet historical trends are largely ignored. This is unfortunate since historical data exist for urban areas and can be used to quantitatively test dynamic models and theory. We maintain that understanding the growth dynamics of a region's past allows more intelligent forecasts of its future. We compare using a spatio-temporal interpolation method with an agent-based simulation approach to recreate the urban extent of Santa Barbara, California, annually from 1929 to 2001. The first method uses current yet incomplete data on the construction of homes in the region. The latter uses a Cellular Automata based model, SLEUTH, to back- or hind-cast the urban extent. The success at historical urban growth reproduction of the two approaches used in this work was quantified for comparison. The performance of each method is described, as well as the utility of each model in re-creating the history of Santa Barbara. Additionally, the models’ assumptions about space are contrasted. As a consequence, we propose that both approaches are useful in historical urban simulations, yet the cellular approach is more flexible as it can be extended for spatio-temporal extrapolation.

Designing Firm Integrating Processes from the Knowledge-Based View—Graduate Student Best Paper Award, CASOS 2002 Conference

The knowledge-based view provides a powerful perspective for analyzing and understanding the firm. However, some of its assumptions have proven difficult to operationalize, making hypothesis testing difficult. One strategically important assumption is that firm competitive advantage depends on effective integrating processes. This paper develops a more precise specification of “effective” integrating processes. Then, two integrating processes are modeled using an agent-based simulation approach to test a set of factors believed to influence knowledge transfer and application. Results support several hypotheses generated by the knowledge-based view including: (1) greater levels of tacit knowledge transfer enhance the performance benefits gained from integrating processes, and (2) integrating processes with greater task uncertainty benefit from more formal information communication. The results also suggest that the design and management of firm integrating processes is a complex undertaking that requires a clear understanding of (1) the primary performance measure(s) used to evaluate the process, (2) the nature of the process tasks (e.g., routine vs. idiosyncratic), and (3) the knowledge required to perform those tasks (e.g., explicit vs. tacit).

Size of Firm, Market Structure, and Innovation

Size of Firm, Market Structure, and Innovation