Simulation Using Events and Goals – A New Approach to Agent-based Modeling

I just finished a research project that was funded through the DARPA Ground Truth program: SCAMP (Social Causality with Agents using Multiple Perspectives). The credit for developing SCAMP goes to my colleague Van Parunak, who had the vision to conceptualize the methodology and the ability to get it funded and to see it through. Van is the President  of ABC Research: Superior Solutions through Agent-Based and Complex Systems. For technical detail on SCAMP go to: Parunak et al. (2020) SCAMP’s Stigmergic Model of Social Conflict. Computational and Mathematical Organization Theory.

The computer science of SCAMP drew me in, but what really interested me was its novel approach to modeling social phenomena. SCAMP looks at network-based phenomena in ways that traditional networking does not. For a non-technical explanation of the possibilities, go to: Social Causality with Agents using Multiple Perspectives: A Novel Approach to Understanding Network-based Social Phenomena.

SCAMP is different from traditional social networks in two fundamental ways. First, nodes are events through which agents pass. In traditional networks the nodes are the agents themselves. Second, SCAMP event networks connect to a second set of networks that represent the goals for each agent group. Because of these differences SCAMP reveals both novel questions and novel perspectives on familiar questions. For a deeper explanation, read on.

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Social Causality with Agents using Multiple Perspectives: A Novel Approach to Understanding Network-based Social Phenomena

PDF version of this post. SCAMP description 12_04_2020

Current Approach to Network-based Social Phenomena
There is a deep body of research and theory on emergent effects in networks. To name but a few, the network lens has been applied to phenomena such as disease transmission, personal behavior, rumor and fake news, innovation adoption, and political and social change. The list could go on and on. A common feature of this research is that it identifies nodes as people or groups (agents), and edges as interaction (e.g. communication, influence) among agents.

SCAMP
SCAMP is a network-based scenario-simulation methodology whose runs can reveal new understanding about known topics of research, and also, reveal hitherto unrealized research questions. SCAMP has this capacity because it treats networks in novel ways. 

People/groups  –> Events
Nodes in SCAMP networks do not represent people or groups. They represent events. Edges in SCAMP do not represent communication or influence. They represent agents’ choices as agents participate in successive events based on their individual preferences, the flow of information that results from their decisions, their actions, and their associations with one another. Relationships among events fall into three categories: 1) agent choice, 2) inhibit, and 3) support. If an event involves movement through physical space, SCAMP can also model that movement, including spatially-based interactions among agents.

Goals
To the extent that traditional network analysis is concerned with what agents “want”, those desires are incorporated into the rules that govern agent behavior. Agents’ goals are not treated as separate entities in their own right. SCAMP includes goal hierarchies that are relevant to each agent group. The degree of goal satisfaction within the hierarchy influences agent decisions as they move from event to event. SCAMP allows different hierarchies for different agents, and also, for linked goals across hierarchies.

Event networks differ from goal networks
In SCAMP the rules that govern agents’ movement across events are different from the rules that govern relationships among levels in goal hierarchies. Event movement is driven by an agent’s “personality” (e.g. preferences, affiliations). The state of a goal network is driven by changes in the degree to which events in the world satisfy various goals.

Event networks and goal networks are connected
Selected events in an event network are connected to selected goals. Event activity influences those goals. Conversely, goal satisfaction influences event activity. The figure is a schematic of a SCAMP model. For the sake of simplicity, it omits differences in types of edge relationships, movement across space, and/or relationships among goals, and various other technical details that would be part of a fully-fledged SCAMP model.

Construction by domain experts, not programmers
SCAMP can be configured by domain experts with no formal programming training.

Example
Imagine an election scenario consisting of five types of agents: 1) advocates of position A, 2) advocates of position B, 3) conspiracy believers, 4) provocateurs, and 5) fact checkers. Based on their “personality”, agents move across events such as: “election is declared fraudulent”, “incumbent accepts defeat”, “extremist groups merge”, “political establishment accepts result”, “supporting and opposing groups clash in city X”, “clashes spreads”, and “violence decreases”. A goal hierarchy for say, fact checkers might culminate with “population accepts fact checkers’ assessments”, supported by a variety of sub-goals.

A SCAMP simulation run would reveal agents’ movements across events, goal satisfaction, and mutual influences between event activation and goal satisfaction. Thus, SCAMP could address questions such as: Under what circumstances will fact checkers and advocates of a specific political position affiliated with each other? Or, if such an affiliation takes place, will each group attain more of its goals? These are network-based questions that have both real-world salience and theoretical implications for understanding political behavior, neither of which could be addressed using traditional network approaches.

SCAMP was developed with DARPA funding under their Ground Truth program. The prime contractor was Parallax Advanced Research.
For more information contact:
Van Parunak  van.parunak@gmail.com  
Jonny Morell  jamorell@jamorell.com

 

Agent-based Evaluation Guiding Implementation of Solar Technology

 

AEGIS: Agent-based Evaluation Guiding Implementation of Solar

DE-FOA-0001496: SOLAR ENERGY EVOLUTION AND DIFFUSION STUDIES II – STATE ENERGY STRATEGIES (SEEDSII-SES)

Business contact:
Mr. Vijay Kohli
President
Syntek Technologies
703.522.1025 ext. 201
vkohli@syntek.org
Technical contact:
Jonathan A. Morell, Ph.D.
Director of Evaluation
Syntek Technologies
734 646-8622
jmorell@syntek.org
Confidentiality statement:This proposal includes information and data that shall not be disclosed outside the Government and shall not be duplicated, used, or disclosed – in whole or in part – for any purpose other than to evaluate this proposal. Howev-er, if a contract is awarded to this participant as a result of – or in connection with – the submission of this information and data, the Government shall have the right to duplicate, use, or disclose the data to the extent provided in the resulting contract. This restriction does not limit the Government’s right to use information contained in these data if they are obtained from another source without restriction. The entirety of this proposal is subject to this restriction.

Introduction

AEGIS (Agent-based Evaluation Guiding Implementation of Solar) demonstrates a novel approach to doing program evaluation: combining agent-based modeling with traditional program evaluation, and doing so continually, as the evaluation work unfolds. We propose to test the value of this approach for evaluating programs that promote the goals of SEEDS II, Topic 1, specifically, “Development of new approaches to analyze and understand solar diffusion and solar technology evolution; developing and utilizing the significant solar data resources that are available; improvement in applied research program evaluation and portfolio analysis for solar technologies leading to clearer attribution and identification of successes and trends.”

The field of evaluation has historically fallen short in providing the conceptual understanding and instrumental knowledge that policy makers and planners need to design better programs, or to identify and measure impact. Our hypothesis, supported by our work to date, is that agent-based modeling can improve the quality and contribution of evaluation. Specifically, we will increase stakeholder involvement and the adoption of evaluation recommendations. We propose to apply and evaluate our approach on programs that are designed to reduce the soft costs of solar deployment and to overcome barriers to diffusion, commercialization, and acceptance.

Scientific Justification and Work to Date

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Integrating Evaluation and Agent-Based Modeling

Over the past year or so I have been thinking a lot about novel methodologies and approaches to help evaluators understand unexpected program behaviors. This is part of my general view that it’s possible to increase the lead time between when indicators of unexpected change first pop up, and when the need arises to adjust evaluation methodologies. And, I believe that the longer the lead time, the better the adjustments.

One result of my ponderings has been the idea that agent based modeling (ABM) should be tightly integrated into traditional program evaluation methods. ABM approaches are desirable for a host of reasons, not the least of which is that they are based on the principles of complex adaptive systems (CAS). Because of this connection, combining ABM and traditional evaluation has two advantages. First, it will help with the “unexpected behavior” problem. Second, it can help shift the way in which CAS is presently used in our business.
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