Network Formation: General Resources
- Last Updated: 5 October 2020
- Albert-László Barabási, "Network Overview"
2006 Keynote Address.
- Professor Albert-László Barabási (Department of
Physics, Notre Dame, Indiana) directs a research group focusing on the
emergence and evolution of networks in various contexts (e.g., metabolic and
genetic networks, actor networks, collaborative networks). This fun slide
presentation provides a vivid visual summary of some of their key findings to date.
- Stephen P. Borgatti and Pacey C. Foster, "The Network Paradigm in
Organizational Research: A Review and Typology", Journal of
Management, Vol. 29(6), 2003, 991-1013. The published article is
- Abstract: This paper reviews and analyzes the emerging
network paradigm in organizational research. It begins with a conventional
review of recent research organized around recognized research streams. It
then analyzes this research, developing a set of dimensions along which
network studies vary, including: direction of causality; levels of analysis,
explanatory goals, and explanatory mechanisms. The latter is used to
construct a 2-by-2 table cross-classifying studies of network consequences
into four canonical types. The paper concludes by noting the rising
popularity of studies placing a greater stress on agency than was traditional
in network research.
- Vincent Cheung and Kevin Cannon, Introduction to Neural Networks,
- Abstract: Superb introduction to artificial neural networks.
- Giorgio Fagiolo (Laboratory of Economics and Management, Sant'Anna School of Advanced studies, Pisa, Italy)
has prepared a ppt presentation, titled
Games on Networks: Rationality, Dynamics, and Interactions (pdf,1.5MB),
that reviews recent research by himself and others on
the endogenous formation of networks by strategically interacting players in a variety of application contexts.
Bernd Fritzke, Growing Self-Organizing Networks - Why?"
In: M. Verleysen, (ed.), ESANN'96: European
Symposium on Artificial Neural Networks, D-Facto Publishers,
Brussels, 1996, 61-72 (invited paper).
"The reasons to use growing self-organizing networks are
investigated. First an overview of several models of this kind is given
are they are related to other approaches. Then two examples are pre-
sented to illustrate the specic properties and advantages of incremental
networks. In each case a non-incremental model is used for comparison
purposes. The rst example is pattern classication and compares the
supervised growing neural gas model to a conventional radial basis func-
tion approach. The second example is data visualization and contrasts
the growing grid model and the self-organizing feature map."
- Sanjeev Goyal (Faculty of Economics, Cambridge University, UKB),
"Learning in Networks"
Working Paper, December 2003.
- Abstract: This paper surveys theoretical (analytical)
research on learning, with a special focus on the interaction network between
individual entities. The survey first examines the nature of learning about
optimal actions for a given network architecture. It then discusses learning
about optimal links and actions in an evolving network.
- Sanjeev Goyal, Connections: An Introduction to the Economics of Networks, Princeton University Press, Princeton and Oxford, 2007.
- Abstract: In (this booKB), Sanjeev Goyal puts contemporary thinking about networks and economic activity into context. He develops a general framework within which this body of research can be located. Topics covered include: network concepts and empirics; games on networks; coordination and cooperation; social learning; social networks in labor markets; strategic network formation; one-sided link formation; two-sided link formation; and research collaboration among firms.
- Matthew Jackson, "Networks in the Understanding of Economic Behaviors"
Journal of Economic Perspectives 28(4), Fall 2014, 3-22.
- Abstract: This study provides an overview of theoretical (analytical) and empirical work on network formation. It does not cover the now-extensive literature on the computational modeling of network formation
- Michael Kosfeld (University of Zurich, Switzerland),
Working Paper, May 2003.
- Abstract: This paper surveys (human-subject) experimental research on social and economic
networks. The experiments consider networks of coordination and cooperation,
buyer-seller networks, and network formation.
David G. Rand, Samuel Arbesman, and Nicholas A. Christakis, "Dynamic Social Networks Promote Cooperation in Experiments with Humans
Proceedings of the National Academy of Sciences, Early Edition, 2011.
- Abstract:The authors conduct human-subject experiments to explore large-scale cooperation, where subjects' cooperative actions are equally beneficial to all those with whom they interact. Their experimental findings confirm the predictions of a set of evolutionary game theoretic models and demonstrate the important role that dynamic social networks can play in supporting large-scale human cooperation.
- Leigh Tesfatsion and Kenneth L. Judd, Eds., Handbook of Computational
Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics
Series, North-Holland/Elsevier, Amsterdam, the Netherlands, Spring 2006.
- Abstract: This handbook includes two chapters on the agent-based
computational modeling of networks, one chapter (by Allen Wilhite) focusing on
economic activities conducted on fixed-node networks and a second chapter (by
Nicolaas Vriend) focusing on the endogenous formation of economic relational
networks. The preface and table of contents of this handbook can be viewed
- Frank Schweitzer, Giorgio Fagiolo, Didier Sornette, Fernando Vega-Redondo, Alessandro Vespignani, and Douglas R. White, "Economic Networks: The New Challenges"
Science Vol. 325, July 24, 2009, 422-425.
"The current economic crisis illustrates a critical need for new and fundamental understanding of the
structure and dynamics of economic networks. Economic systems are increasingly built on
interdependencies, implemented through trans-national credit and investment networks, trade relations, or
supply chains that have proven difficult to predict and control. We need, therefore, an approach that
stresses the systemic complexity of economic networks and that can be used to revise and extend
established paradigms in economic theory. This will facilitate the design of policies that reduce conflicts
between individual interests and global efficiency, as well as reduce the risk of global failure by making
economic networks more robust."
- Randall Verbrugge (BLS, Washington D.C.), "Interactive Agent Economies:
An Elucidative Framework and Survey of Results", Macroeconomic
Dynamics 7(3), 2003, 424-472.
- Abstract: The author presents an analytical framework for exploring the
implications of economic interaction. The initial sections of the paper
include a survey of previous work on interacting agent economies with a
stress on analytical studies.
- Anna Nagurney (John F. Smith Memorial Professor, Isenberg School of
Management, University of Massachusetts, Amherst) is the Editor of a book series titled New Dimensions in Networks, to be published by Edward Elgar Publishing Company. This series is designed to publish original manuscripts and edited volumes that push the development of the theory and application of networks to new dimensions. It is interdisciplinary andinternational in its coverage, and aims to connect existing areas, unveil new applications, and extend existing conceptual frameworks as well as methodologies. For information regarding manuscript submission policy and
- Agent-Based Computational Research on the Evolution of Interaction Networks
- Leigh Tesfatsion (Iowa State University, Ames) maintains a resource
site for researchers interested in taking an agent-based computational
economics (ACE) approach to studying the
Evolution of Interaction Networks.
ACE is the computational study of economies modeled as dynamic systems of interacting agents. The site provides annotated pointers to
readings, software, individual researchers, and research groups.
- CASOS: Center for Computational Analysis of Social and Organizational
CASOS (Center for Computational
Analysis of Social and Organizational Systems)
at Carnegie Mellon University brings together computer science, dynamic
network analysis, and the empirical study of complex socio-technical systems.
Computational and social network techniques are combined to develop a better
understanding of the fundamental principles of organizing, coordinating,
managing, and destabilizing systems of intelligent adaptive agents (human and
artificial) engaged in real tasks at the team, organizational, or social
level. CASOS is a university-wide center drawing on faculty, students, and
programming staff in multiple departments at Carnegie Mellon.
- Coalition Theory Network
Coalition Theory Network (CTN)
is an association of high-level scientific institutions whose aim is the
advancement and diffusion of research in the area of coalition formation.
The CTN, founded in 1995, sponsors annual meetings and summer school
- Complex Systems and Random Networks (Mendes)
- J. F. F. Mendes (University of Aveiro, Portugal) maintains a resource
Complex Systems and Random Networks
that provides pointers to publications, discussion papers, and links related to this topic area.
- Economics of Networks (Economides)
Internet Site for the Economics of Networks,
maintained by Nicholas Economides (New York University), provides a
collection of information on economic issues of networks, such as
the telephone and fax communications networks, the internet,
financial exchange and credit card networks, and "virtual networks"
such as the virtual network of all Windows or all Mac computers.
- Economics of Networks Links (WebEc)
- WebEc maintains a list of annotated pointers focusing on the
Economics of Networks.
- Emergence (Resnick and Silverman)
- Mitchel Resnick and Brian Silverman (Epistemology and Learning
Group, MIT Media Laboratory) maintain an "active essay" that explores the
idea of emergence of global regularities arising from simple
interactions. Visitors with a Java-enabled browser can activate animations
that illustrate the concepts under discussion. The site can be accessed
- e-Social Science Project
- A group of researchers at the University of Manchester,
managed by Gillian Sinclair, is conducting research on e-Social
Science, the application of grid technologies to social science
research, including economics. They have funded several projects in
qualitative data and one of special interest to economists entitled
FINGRID. This group is also hosting the First International
Conference on e-Social Science at Manchester this summer. For more information, visit
- Graph Theory and Network Analysis (Batagelj)
- Vladimir Batagelj maintains a list of pointers to
graph theory and network analysis resources
(e.g., programs, algorithms, graph formats, data, and visualization tools).
- International Network for Social Network Analysis (INSNA)
International Network for Social Network Analysis (INSNA)
contains information about INSNA and related subjects, including reference
sources, links to related sites, and network software.
- Network Dynamics Bibliography (Crutchfield and Watts)
Network Dynamics Bibliography
is an on-line bibliography maintained by Jim Crutchfield and Duncan Watts at
the Santa Fe Institute that lists pointers to Web sites, books, papers, and
reviews related to networks and network dynamics.
- Network Economics (Varian)
is a site maintained by Hal Varian at the University of Berkeley. It
provides a briefly annotated list of pointers to Web sites focusing on
network issues such: as the impact of the Internet on people, firms, and
markets; new economic forces at work in the ever more networked global
economy; telecommunications issues (including the Microsoft anti-trust case);
network externalities; and technical specs and services.
- Networks and Social Dynamics
Social Dynamics Laboratory Group
at Cornell University studies the effects of network topology on the dynamics
of social interaction.
- Network Effects and Lock-In (Liebowitz)
maintained by Stanley J. Liebowitz (University of Texas at Dallas) is devoted
to network effects, path dependence, and lock-in. In particular, the site
provides reviews, commentary, and resources related to a book by Leibowitz
et al. titled Winners, Losers, and Microsoft (Independent Institute,
March 2001). In the latter, the authors provide a detailed empirical
critique of the network effects, path-dependence and lock-in purported to
have occurred in the software industry as well as in other often-cited cases
(e.g., the triumph of the QWERTY configuration over the Dvorak configuration
for typewriter keyboards, and the triumph of the VHS format over the Beta
format for videocassette recorders).
- Scale-Free Networks and Small World Networks (INSNA)
International Network for Social Network Analysis (INSNA)
maintains links to papers and other research resources related to
scale-free (power law) networks and small world networks.
- Self-Organized Networks (Barabási)
- Professor Albert-László Barabási (Department of
Physics, Notre Dame, Indiana) conducts research focusing on the
emergence and evolution of networks in various contexts (e.g., metabolic and
genetic networks, actor networks, collaborative networks). For more information about his research, visit
- Self-Organizing Innovation Networks (Gilbert)
- Nigel Gilbert (University of Surrey, UK)
conducts research on self-organizing innovation networks.
For more information, visit
A key paper on this topic is Nigel Gilbert
(University of Surrey, UKB), Andreas Pyka (University of Augsburg, Germany),
and Petra Ahrweiler (University of Hamburg, Germany),
"Innovation Networks -- A Simulation Approach"
Journal of Artificial Societies and Social
Simulation, Volume 4, No. 3, 2001.
- Social Network Analysis (Snijders)
Social Network Analysis Page
is maintained by Tom A. B. Snijders (Department of Sociology, University of
Groningen, the Netherlands). The site provides annotated pointers to a
variety of downloadable programs for social network analysis developed by
Snijders and his collaborators, together with pointers to related articles.
- Social Network Links (White)
- Doug White (Anthropology and Social Science, UC Irvine, Irvine, CA)
maintains a listing of
Social Network Web Sites
that includes galleries, projects, vitae, home pages, articles, journals,
associations, newsletters, listservers, and classes related to social network
- Virtual Center for Supernetworks (Nagurney)
- A supernetwork is a network consisting of nodes, links (virtual or
physical), and flows that is over and above a collection of existing
networks. Examples include teleshopping and telecommuting networks. The
Virtual Center for Supernetworks at the University of Massachusetts, Amherst,
is directed by Anna Nagurney (John F. Smith Memorial Professor, Isenberg
School of Management). The primary objective of this interdisciplinary
center is to foster the study and application of supernetworks and to serve
as a resource to academia, industry, and government on such networks.
Resources available at this site include project descriptions, a compilation
of software tools, and pointers to related links. For more information,
- Visualization of Networks
Visual Complexity Site
provides an astonishing array of network visualizations created by complexity researchers. Types of visualized networks include art, business, computer systems, food webs, the Internet, knowledge networks, music, pattern recognition, political networks, social networks, transportation networks, and the World Wide Web.
Software, Toolkits, and Computer Demos
- Boids - Flocking Creatures (Reynolds)
- Craig Reynolds (Sony, Research and Development Group) maintains a
web site titled
featuring his simulations of flocking creatures called "boids." His basic
flocking model consists of three simple steering behaviors possessed by each
individual boid that govern how each boid maneuvers itself based on the
positions and velocities of its nearby flockmates. As illustrated by the
Java applets at this site, the model results in amazingly life-like
collective flocking dynamics. Also available at this site is a link to work
by Reynolds on an interactive system permitting user interaction with large
groups of autonomous characters. The characters respond in real time to the
user's interaction as well as to each other and their environment.
- Computational Laboratories and Demonstration Software (Tesfatsion)
- Leigh Tesfatsion (Economics Department, Iowa State University, Ames)
maintains a list of annotated pointers to
ACE/CAS Computational Laboratories and Demonstration Software.
Included are many demos of possible interest for the agent-based
computational modeling of networks (e.g., demos for several distinct versions
of the Schelling Segregation Model, a Trade Network Game demo, demos of
cellular automata, and demos of flocking behavior).
- Multiple-Agent Modeling Software and Toolkits (Tesfatsion)
- Leigh Tesfatsion (Economics Department, Iowa State University,
Ames) maintains an extensive site
providing annotated pointers to general software and toolkits designed
specifically for the modeling of systems with multiple interacting agents.
Several of the toolkits, for example
provide specific capabilities for the representation and visualization of
- Network Analysis Software (Benta)
- Marius Benta (University College, Cork, Ireland) has developed
free application software called Agna for social network analysis,
sociometry, and sequential analysis. The purpose of the software is to
assist in the study of group communication relations, kinship relations,
the structure of animal behavior, and organizational psychology. For more
- Network Visualization (Kempel)
- Lothar Kempel (MPI für Gesellschaftsforschung, Köln,
NetVis: A Gallery of Social Structures.
This network visualization site documents work in progress regarding efforts
to visualize social structures using a combination of automatic procedures
- Network Visualization for Real-World Systems
maintains an intriguing site devoted to the visual exploration of real-world complex networks.
- Neural Network Toolbox (Mathworks)
- The Mathworks, Inc., has released version 4.0.1 of its Neural Network
Toolbox (NNT) for the design and use of artificial neural networks in various
practical application settings (e.g., banking and finance, business, credit
card activity checking, defense, engineering, electronics, entertainment,
industrial, insurance, manufacturing, medical, oil and gas, robotics, speech,
securities, telecommunications, and transportation). A printable version of
the NNT user's guide is available online in pdf format that provides an
introduction to neural networks, help with NNT installation, a discussion of
NNT capabilities, and sample applications. For more information, visit
- ORA: Social Network Analysis Tool (CASOS)
- ORA is a social network analysis tool that enables the user to
simultaneously reason about multiple networks connecting people, knowledge,
resources, and tasks (or events). Both traditional and dynamic network
measures are included. ORA can be used for risk assessment to locate
individuals that are potential risks to the group or organization given one
or more of the following types of relational or network information: social;
knowledge; resource; and task/event. The GUI is used to set up
organization(s) and perform two broad functions: run risk measures on the
organization(s); and optimize the organizational structure. ORA is supported
by CASOS, the Center for Computational Analysis of Social and Organizational
Systems at Carnegie Mellon University. For more information, visit
- Schelling Segregation Model (Cook)
- Chris Cook (Computer Science Department, Iowa State University,
Ames) has developed an interactive computer demo for an extended version of
the Schelling Segregation Model (SSMB), due to Thomas Schelling
(Micromotives and Macrobehavior, Norton, 1978). Agents are located on
a chess board with 64 locations. The user determines the population mix from
among three agent types (red, green, and blue), or chooses from among various
default settings. The user can also specify a "happiness rule" for
each agent type or select a default setting. The happiness rule determines
when an agent is happy with his current board location, taking into account
both the number and the types of his neighbors. If unhappy, the agent either
attempts to move to a more desirable board location or exits the board
- Chris Cook has released his SSM demo as freeware under the GNU
Public License. Automatic installation software for his SSM demo can be
The Schelling Segregation Model: Demonstration Software
Also available at this site is a more detailed description of the SSM, a
description of the SSM demo's capabilities, instructions for using the
automatic installation software, a link for accessing the SSM demo
source code (C#), and copyright information.
- Trade Network Game Lab (Tesfatsion)
- The Trade Network Game Laboratory (TNG Lab) is a
computational laboratory for exploring the evolution of trade networks among
strategically interacting buyers, sellers, and dealers. The TNG Lab is
targeted for the Microsoft Windows desktop and is both modular and
extensible. It permits visualization of the formation and evolution of trade
networks by means of run-time animations, run-time charts, and run-time data
displays. A clear, easily operated graphical user interface permits testing
of key parameters pertaining to learning, payoffs, types and numbers of
agents, and the physics controlling network displays. Automatic installation
software for the TNG Lab, together with tutorials and research articles, can
be obtained at the
TNG Home Page.
Books and Journals
Books and Monographs on Economic and Social Network Formation
Journal of Economic Interaction and Coordination
Netnomics: Economic Research and Electronic Networking
Review of Network Economics
Some Early Individual Researchers
Important Disclaimer: Research on social network formation is now so extensive it is impossible to keep the links below either current or complete. These links were last thoroughly updated in 2009 and can be viewed as an historical record of some early researchers in this area.
John E. Abraham,
(Civil Engineering, University of Calgary, Calgary, Canada):
Microsimulation of urban economic and transportation systems for
transportation planning, urban planning and policy analysis. Part of a team
of Canadian researchers focusing on locational decisions of firms and
households and the related decisions of land developers, and how these are
influenced by the economic flows/trips that occur among given locations.
Howard E. Aldrich
(Sociology, University of North Carolina, Chapel Hill, U.S.A.):
Entrepreneurship; Origins of new organizational populations; Organizational
(Psychology, University of Oregon, Eugene): Human-subject experiments with
endogenous formation of socioeconomic networks; Social psychology
(Economics, McGill University, Montreal, Canada): Noncooperative theory of
network formation; learning from neighbors; a strategic model of network
(Physics, University of Notre Dame, Indiana): Networks; Internet; Cellular
Networks; Parasitic computing.
Jennifer L. Berdahl
(Rotman School of Management, University of Toronto):
The dynamics of composition and socialization in small groups -- insights
gained from a computational model; A theory of groups as complex systems;
Dynamics of diversity in work groups
(Professor Emeritus, Sociology, University of California, Los Angeles): Social networks; Evolution of exchange networks.
(Organization Studies, Boston College, Massachusetts): Social networks; Knowledge flows in
organizations; Network methodology.
(Department of Economics, University of Laval, Quebec, Canada): Interdependent utilities and
(Economics, ICREA, Universitat Autonoma de Barcelona and CEPR):
Bargaining networks; Referral networks; Formation of socioeconomic networks.
Kathleen M. Carley
(School of Computer Science, Carnegie Mellon University, Pittsburgh, PA): Computational and
social and organization theory; dynamic social networks; multi-agent network models; group,
organizational, and social adaptation and evolution; statistical models for dynamic network
analysis and evolution; computational text analysis; and the impact of telecommunication
technologies on communication and information diffusion within and among groups.
(Economics, Columbia University, N.Y.): Trade networks
(Economics, University of Texas, Austin): Directed matching and monetary
exchange; Endogenous market participation.
(Economics, Universitat Pompeau Fabra, Barcelona): Bilateral trading networks
modelled as bargaining models under rigid communication
(Geography, University of Maryland, College Park): Agent-based simulation;
Computational laboratories in economic geography; Formation and effects of
socio-economic networks in spatial landscapes; Small-world networks.
(Faculty of Social and Behavioural Sciences, University of Amsterdam, the Netherlands):
Land use transportation planning and policy; Long-term effects of multi-modal transportation
infrastructure planning and
pricing policy in relation to the residential choice behavior of households;
Agent-based simulation within the framework of the AMADEUS research program.
(Economics, Warwick University, UKB): Endogenous formation of
networks; Analyzing conflict between stability and efficiency in networks.
Victor M. Eguiluz
(IMEDEA, Universitat de les Illes Balears, Palma de Mallorca, Spain): Dynamical models
of socio-economical network formation and evolution.
(Center for the Study of African Economies, Oxford University, UK): Networks,
communities, and markets in Subsahara Africa; Risk sharing in networks in
rural Philippines; Market emergence, trust, and reputation.
(St. Anna School of Advanced Studies, Pisa, Italy): ACE Labor market
dynamics; Local interaction models; Evolution of social and economic
networks; Learning; Endogenous interactions; Economics of innovation and
Linton C. Freeman
(Sociology and Institute for Mathematical Behavioral Sciences, University of
California at Irvine): Social network analysis; Visualizing social networks;
Uncovering organizational hierarchies.
(Department of Sociology, University of Surry, UK): Innovation networks;
Simulation in the social sciences; Sociology of the environment and science
(Department of Economics, University of Essex, Colchester, UK):
Noncooperative theory of network formation; learning from neighbors;
Collaboration and competition in networks; Strategic analysis of network
(Computer Science, Brown University, Providence, RI): Learning in network
contexts; Automated buyer search on electronic markets; Strategic dynamic
pricing by software agents; Game theory.
(GREQAM, Aix-Marseille University, France): Co-evolution of individual behaviors and interaction structures; Networks and markets; Dynamics of collaboration networks
Joseph E. Harrington, Jr.
(Economics, The Johns Hopkins University, Baltimore, Maryland, U.S.A.):
Endogenous networks; Centralization versus decentralization in multi-unit
organizations; Progressive ambition, electoral selection, and the creation of
(Institute for Economics and Traffic, Dresden University of Technology,
Germany): Concepts from physics applied to the study of supply networks and
business cycles; Pedestrian and vehicle traffic; Sociodynamics and game
(Faculty of Economics, Hosei University, Tokyo): Formation of communities by
natives and newcomers; Network design
Matthew O. Jackson
(Economics, Stanford University, CA): Strategic models of social and economic
networks; Evolution of social and economic networks; Coalition and Party
Formation in legislative voting games; Reputation versus social learning.
(Economics, Sam M. Walton College of Business, University of
Arkansas, Fayettesville): Institutional foundations of industrial
organization; Endogenous business networks as a response to inadequate legal
and financial institutions; The role of business networks in the process of
economic development; Financial interlinkage and assortative matching.
(Resource Economics, University of Nevada-Reno): Spatial economics; Computable
general equilibrium modelling
(GREQAM - Groupement de Recherche en Économie Quantitative d'Aix
Marseille, France): Market organization and trading relationships; Trade
network structures; Endogenous interactions.
(Economics, University of Maryland, College Park): A theory of
buyer-seller networks; Vertical integration, networks, and markets.
(Organizational Consultant, Organizational Network Anaysis, DotCom):
Building adaptive organizations in the networked knowledge economy;
Organizational network mapping; Terrorist networks.
Michael W. Macy
(Sociology, Cornell University, Ithaca, N.Y.): Informal social control in
on-line trading communities; Coalition formation in exchange networks;
Trust and cooperation in the U.S. and Japan; Management fads; Collective
action; Evolutionary game theory; Deviance and social control; Social
psychology; Social Exchange theory; Rational choice.
(School of Informatics and Department of Computer Science, Indiana
University, Bloomington): Evolutionary agents to model societies and
organizations; Referral networks in labor markets; Management networks in
(Computer Science, TU Berlin): Large-scale agent-based microsimulations for
transportation planning; Simulation of the economic decision-making that
leads to demand for transportation; General micro-simulation of
Matthew G. Nagler
(Economics, Lehman College, City University of New YorKB): Network effects of sport utility vehicles; Negative externalities that breed network externalities (i.e. "stick networks" as opposed to "carrot networks"); Consumer behavior.
(Finance and Operations Management, University of Massachusetts,
Amherst, Massachusetts): Network models of large-scale financial,
transportation, and regional economic systems; Algorithms on serial and
parallel computer architectures to predict flows of funds, people, goods, and
John M. Orbell
(Professor Emeritus, Political Science, University of Oregon, Eugene): Evolution of
cooperation and trust; Coordination issues and social chess; Intersection of
evolutionary theory, cognitive science, and the study of human social
relations; Evolutionary psychology.
(Geography, Penn State University, University Park): Cellular automata and
graph based models applied to urban spatial phenomena; Internet geography;
Geocomputation and agent-based modelling.
(Center for Research in Economics and Management, University of Rennes, France):
Global and local effects of interaction structures; Network externalities;
Small-world networks, phase transitions, and avalanches in ACE frameworks;
Moduleco (an agent-based computational laboratory); Cognitive economics;
Generic properties of complex adaptive systems.
Margaret M. Polski
(Institute for Development Strategies, Indiana University, Bloomington, and
A. T. Kearney, New YorKB): Agent-based modelling; Economic development and
institutional change; Innovation and growth in the new economy; Institutional
evolution and change in U.S. commercial banking; Legislative games.
James E. Rauch
(Economics, University of California at San Diego, La Jolla, California):
Impact of bureaucratic structure on bureaucratic and economic performance;
Incomplete information and networks in international trade; Networks and
(Computer Science, Iowa State University, Ames): Graph theory;
networks and game theory.
Tom A. B. Snijders
(Department of Sociology, University of Groningen, the Netherlands):
Evolution of social networks; Statistical methods; Simulation models; Random
utility; Markov chain Monte Carlo; Simulation-based estimation.
(Center for Research in Economics and Management, University of Rennes, France):
Local interaction models; Social
capital; Social networks; Spatial dynamics.
(Economics, Iowa State University, Ames, Iowa): Agent-based
computational economics; The design of restructured wholesale power markets;
A computational laboratory for visualizing and analyzing the formation of
buyer-seller trade networks under alternative market structures; Market
power, hysteresis, and excess earnings heterogeneity in labor markets arising
from network and behavioral effects.
(Economics, University of Pittsburgh, PA): Directed matching and monetary
exchange; Search equilibrium; Learning in market games.
(Kellogg Graduate School of Management, Northwestern University, Evanston):
How professionals, entrepreneurs, and firms develop and use social networks to
make markets and manage transactions.
Anne van de Nouweland
(Economics, University of Oregon, Eugene): Link formation in cooperative
(Facultad de Económicas, Universidad de Alicante, Spain): Learning in
games; Evolution; Networks; Complex dynamics.
(Sociology and Social Anthropology, Central European University): Social and economic transformation from
a network perspective; The analysis of the sequences of network events;
Inter-organizational and intra-organizational networks; Conceptual and
(Economics, Queen Mary and Westfield College, University of
London): Dynamics of interactive market processes; Emergent properties of
evolving market structures and outcomes.
(Anthropology and Social Science, UC Irvine, Irvine, CA): Social networks
and sociocultural complexity; longitudinal fieldsite and network ethnography;
(Economics, University of Alabama in Huntsville): Small-world networks;
Decision making when agents are influenced by the decisions of others.
Ian F. Wilkinson
(Marketing, University of Sydney, Australia): Evolution
of institutional and network structures; Structural dynamics of industrial
networks; the Kauffman NK model.
Randall D. Wright III
(Economics, University of Pennsylvania, Philadelphia): Dynamic matching in
monetary exchange; Pricing and matching with frictions; Search equilibria.
Martin G. Zimmermann
(Department of Physics, University of Buenos Aires): Dynamical models of
socio-economic network formation and evolution.
Copyright © Leigh Tesfatsion. All Rights Reserved.