Construct - A Multi-Agent Network Model for the Co-Evolution of Agents and Socio-Cultural Environments

Schreiber, Craig; Singh, Siddhartha; Carley, Kathleen M.

doi:10.21236/ada460028

Cited by 44 publications

(43 citation statements)

References 24 publications

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“…Though there have been several past efforts to develop a Construct general interface [2,11], this work was under-appreciated and not widely used. In January 2007, work began on a graphical user interface for Construct, and as of this publication the tool is still under development.…”

Section: Resultsmentioning

confidence: 99%

Specifying Agents in Construct

Hirshman

Carley

Kowalchuck³

2007

SSRN Journal

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Section: Resultsmentioning

confidence: 99%

Specifying Agents in Construct

Hirshman

Carley

Kowalchuck³

2007

SSRN Journal

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“…Agents which have more knowledge in common have been shown to be more likely to interact. Such a phenomenon, called relative expertise [20], is an extension of the concept of homophily, which states that the more similar two people are, the more likely they are to interact [35].…”

Section: Related Workmentioning

confidence: 99%

“…During such a turn, the agent will pick at most one other agent to interact with, according to a well-defined CDF generated by using the concept of relative expertise [20].…”

Section: Simulation Model O Verviewmentioning

confidence: 99%

Simulating Diffusion with Conflicting Knowledge

Joseph

Carley

2012

SSRN Journal

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assistance on the ideas presented in this work.Keywords: Agent-based modeling, Diffusion Processes, Decision-making. AbstractA simulation model is developed and used to better understand the diffusion of conflicting knowledge in a social network. Within the simulation, correct and incorrect knowledge diffuse simultaneously, however, simulation agents are not explicitly made aware that contradictory information exists. Instead, they may become aware of this by obtaining both correct and incorrect knowledge of the same piece of information. The agent must then determine what knowledge to accept. We implement various models of how people react to conflicting knowledge, which we refer to as intrapersonal conflict resolution strategies (ICRS). We analyze these ICRS with respect to the speed and breadth of correct and incorrect knowledge spread and how often agents find themselves holding conflicting knowledge. We also consider the effect of varying the odds of incorrect knowledge existing in the system, the network structure and the differentials in trust between agents. We find evidence that our model is consistent with many real-world notions of diffusion. Consequently, we make tentative conclusions about the effect of our various experimental conditions on the spread of conflicting knowledge in a social network. We note that conflicting knowledge spreads more slowly and persists longer than knowledge which cannot be conflicted.

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“…Network evolution and the diffusion of information and beliefs through social networks can be examined using Construct (Carley, 1995;Hirshman & Carley, 2007b, Hirshman, Martin & Carley, 2008. Construct captures group dynamic dynamics under diverse cultural and technological configurations (Schreiber & Carley, 2004). Consequently, organizational change (Carley & Hill, 2001), socio-cognitive inconsistencies (Carley & Krackhardt, 1996), the impact of communication technologies (Carley, 1995;Carley 2002) can be tested with Construct.…”

Section: Constructmentioning

confidence: 99%

Interfacing Network Simulations and Empirical Data

Lospinoso¹,

McCulloh²,

Johnson³

2009

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY) May 2009 REPORT TYPE Technical Report DATES COVERED (From -To PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NUMBERDepartment of Mathematical Sciences, U.S. Military Academy n/a SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)USMA NSC USMA Network Science Center SPONSOR/MONITOR'S REPORT NUMBER(S) 09-001 DISTRIBUTION / AVAILABILITY STATEMENT Unlimited Distribution SUPPLEMENTARY NOTESThe views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. ABSTRACTThis project tests Social Network Models for longitudinal data against empirical data using an original statistical test to determine the effectiveness of various models at reproducing networks. The Link Probability Model (LPM) is introduced as a viable model for the reproduction of social networks in dynamic equilibrium. We survey social network simulation packages and find that Construct uses a continually updated LPM as its stochastic engine, further establishing the LPM's viability as a social network model. We use actor oriented models to estimate statistically significant behavior on empirical networks and provide guidance on future extensions into multi-agent simulation, which is a rapidly growing area of research. Our findings rely on various empirical datasets and provide analytical results on the nature and structure of the social networks observed in them. Social network analysis (SNA) is the mathematical methodology of quantifying connections between individuals and groups. It has become an important analytic tool for analyzing terrorist networks, friendly command and control structures, arms trade, biological warfare, and the spread of diseases, among other applications. This analysis provides a wealth of information about how individuals in a network interact with each other. Much of the power of SNA is derived from our ability to make prescriptions and predictions about network behavior. There are advanced simulation packages readily available to conduct th...

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Construct - A Multi-Agent Network Model for the Co-Evolution of Agents and Socio-Cultural Environments

Cited by 44 publications

References 24 publications

Specifying Agents in Construct

Specifying Agents in Construct

Simulating Diffusion with Conflicting Knowledge

Interfacing Network Simulations and Empirical Data

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