News Category: ‘Past Friday Seminars’

Friday, February 11 – 3:00 p.m.

What Leads to Innovation? An Analysis of Collaborative Problem-Solving

Randy M. Casstevens
CSS PhD Candidate
George Mason University

Collaboration within large groups of individuals is difficult to analyze due to the large number of interactions involved. This presentation will explore the problem-solving process of software developers participating in programming contests with three types of analysis. First, regression was used to investigate the effect of two problem characteristics, size and modularity, on the innovation process. With a better understanding of how the characteristics of the problem being solved influences the rate of innovation, development obstacles may be avoided. Second, the distributions of developers per project and projects per developer were calculated to establish if they have heavy tailed distributions like seen in the open source community. This will explore parallels with the open source community and examine whether heavy tailed distributions can form over a relatively short time period, approximately one week. Finally, the programming contests were examined to look for evidence of creative destruction. In creative destruction, the introduction of a new innovation causes other products to no longer be used and was credited by Schumpeter as the “fundamental impulse that sets and keeps the capitalist engine in motion” (Schumpeter, 1942, pg. 83).

Friday, February 18 – 3:00 p.m.

TITLE: Exploring the Identification Question for Parameterized Social Simulation Models

Steven Wilcox
CSS PhD Student
George Mason University

Progress in bridging the gap between computational social science and conventional quantitative social science methodology will facilitate the application of simulation to practical issues involving a societal dimension and improve the level of rigor in social science methodology. Specifically, we examine the identification question as applied to simulation models rather than regression-type models. Thus, rather than plugging parameter values into a simulation model to get output measures, we consider the inverse problem in which the objective is to find parameter values that result in the observed data correlations. The identification question is whether such an inverse solution is uniquely available. Using linear regression to estimate metamodels of these correlations, estimates of the inverse problem can be found. Moreover, the resulting computations allow one to analyze the identification issues for a given simulation model in terms of its ability to generate a match to the calibration data.

To address the gap we utilize at an agent-based model of neighborhood crime as an example. In it, seemingly small differences in the simulation logic can make a big difference in the output measures. Merely having many parameters in the simulation model is not sufficient to permit a good match, as can be seen using data visualization techniques. Once the simulation model yields a range of output measures that bracket the data, we can then consider the calibration step. However, a great deal of work needs to be done in terms of demonstrating the computational effectiveness of candidate calibration algorithms and validating their results statistically.

Friday, February 25 – 3:00 p.m.

An Introduction to Survey Methodology

Steve Scott
CSS PhD Student
George Mason University

Social science research often requires the use of surveys to obtain information on attitudes, preferences, or personal information from large numbers of respondents. For all but the smallest of populations, it is impractical or infeasible to perform a complete census of the entire target population, so a subset must be chosen and surveyed instead. The way in which the target population is partitioned, however, can introduce unwanted effects on the data obtained. This presentation provides an introduction to the science of survey methodology, which is focused on methods used to identify and minimize possible sources of error when sampling a population. Topics include inference and error in surveys, target populations, sampling frames, and types of coverage errors, sample design and sampling error, methods of data collection, and applications of survey methodology techniques for large scale social science modeling applications.

Friday, March 4 – 3:00 p.m.

Social Network of the Human Brain

David Franks
Professor Emeritus
Department of Sociology
Virginia Commonwealth University

Abstract forthcoming.

Friday, April 8 – 3:00 p.m.

Understanding Current Events at the Fukushima Dai-ichi Nuclear Power Plants

William Kennedy
Research Assistant Professor
Center for Social Complexity
George Mason University

Bill Kennedy will provide a background for understanding current events. In one hour, he plans to review the relevant nuclear physics, nuclear engineering, nuclear physiology, and current events at the Fukushima Dai-ichi nuclear power plants. Group discussion will follow.

Bill Kennedy was a nuclear engineer in the Navy, a senior member of the Nuclear Regulatory Commission’s staff, and the Technical Director in the office of New Production Reactors at the Department of Energy.

Friday, April 15 – 3:00

Collective-Action and Exchange: A Game-Theoretic Approach to Contemporary Political Economy

William Ferguson, Professor
Department of Economics
Grinnell College

Abstract:Development (economic or otherwise) requires resolution of collective-action problems (CAPs)– divergences between the pursuit of individual interest or inclinations and the well-being of some group. CAPs underlie both market success and market failure. Using collective-action as a unifying concept, along with a predominantly game-theoretic approach—classical, behavioral, evolutionary and epistemic, complemented somewhat with agent-based modeling—this book develops modeling foundations for a contemporary approach to political economy. In so doing, it unifies many cutting-edge theoretical developments including information economics, behavioral economics, institutional theory, and network theory. More specifically, these developments address asymmetric information, exercises of power, social preference, bounded rationality, social norms, formal institutions, relationships between self-organization and third-party enforcement along with social, political, and economic networks, evolutionary emergence, the conduct of policy, and prospects for economic development. A flexible game-theoretic approach facilitates drawing connections among these diverse concepts.

Chapter 6: Foundations of Motivation: Social Preference and Rationality: Social preference can motivate cooperation and enforcement. A cognition-difficulty gap distinguishes substantive rationality from bounded rationality. Models indicate the importance of framing.

Part III: Informal and formal institutions generate cognitive understandings that facilitate the coordination and enforcement needed to resolve CAPs.

Chapter 7: Informal Institutions: Conventions, social norms, and informal rules coordinate cognition and behavior. Social norms influence social preference and identity concepts.

Chapter 8: Internal Resolution via Group Self-Organization: Group self-organization can sometimes resolve CAPs, with attendant strengths and weaknesses.

Part IV: Complex Networks and Dynamics of Collective Action: Network theory allows representation of systemic interactions and spillovers, potentially illustrating the ecology of political economy. Agent-based modeling can represent dynamic, generative political-economic processes related to CAPs and economic development.

Chapter 10: Networks and Economics: Network models can represent communication, market exchange, internal firm transactions, and the spatial location of economic activity with implications on development, power, and growth.

Chapter 11: Agent-Based Models and the Emergence of Resolution or Lack Thereof
Topics include: the evolution of norms of fairness, within-group provision of public goods, residential segregation, worker prejudice, and financial instability.