Brian Tivnan

Data farming coevolutionary dynamics in RePast

This paper describes the application of data farming techniques (Brandstein and Home 1998) to explore various aspects of revolutionary dynamics (McKelvey 2002) in organization science. Data farming is an iterative process using high-performance computing to execute and vary agent-based models, collect and explore statistical results, and integrate these results for the purposes of growing more data by virtue of generative analysis. The tool of choice for creating these agent-based models is the University of Chicagos Social Science Research Computings (2004) Recursive Porous Agent Simulation Toolkit (Re-Past). The paper concludes with a brief description of Tivnans (2004) Coevolutionary model of boundary-spanning Agents and Strategic Networks (C-BASN), an extension of Hazy and Tivnans (2004) Model of Organization, Structural Emergence, and Sustainability (MOSES).

Market Liquidity and Heterogeneity in the Investor Decision Cycle

During liquidity shocks such as occur when margin calls force the liquidation of leveraged positions, there is a widening disparity between the reaction speed of the liquidity demanders and the liquidity providers. Those who are forced to sell typically must take action within the span of a day, while those who are providing liquidity do not face similar urgency. Indeed, the flurry of activity and increased volatility of prices during the liquidity shocks might actually reduce the speed with which many liquidity providers come to the market. To analyze these dynamics, we build upon previous agent-based models of financial markets to develop an order-book model with heterogeneity in trader decision cycles. The model demonstrates an adherence to important stylized facts such as a leptokurtic distribution of returns, decay of autocorrelations over moderate to long time lags, and clustering volatility. We show that the heterogeneity in decision cycles can increase the severity of market shocks, and even absent a shock can have notable effects on the stochastic properties of market prices.

Systems Engineering Solution for Technology Deployment to Combat Complex Threat Scenarios in the Homeland

Systems engineering has been applied to the problems faced by DHS, including defending the homeland against a complex threat. An initiative currently underway by DHS S&T will use systems engineering to perform analysis against a scenario prior to the field experiment. The systems engineering analysis will help define the specific configuration, location, and Concept of Operations (CONOPs) of the system. Results from the experiment will be fed back into the systems engineering framework architecture in order to refine and tune the analysis. This will give the systems engineers a higher confidence in the analysis and recommendations in future experiments as the system is improved, new sensors are added, and the threat becomes more complex.

Modeling organizational adaptation: a replication of Levinthal's model of emergent order

Levinthals application of Kauffmans 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 Kauffmans NK model to organization science, Levinthas 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 Levinthas 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.