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 short non-technical explanation of the possibilities, go to: Social Causality with Agents using Multiple Perspectives: A Novel Approach to Understanding Network-based Social Phenomena. For the slides of a presentation on SCAMP go to: SCAMP overview and description.

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.

Current Approach to Network-based Social Phenomena

There is a deep body of research and theory on emergent effects and adaptivity in networks (Goldstone, Roberts, & Gureckis, 2008; Gross Thilo & Blasius, 2008. To name but a few specifics, the network lens has been applied to phenomena such as disease transmission (Jovanovski, Tomovski, & Kocarev, 2021), rumor and fake news (Song, Ning, Zhang, & Wu, 2021), innovation adoption (Reia, 2020) the history of religion (Becker, Hsiao, Pfaff, & Rubin, 2020), the dynamics of social change), (Ferguson, 2017), and urban infrastructure (Karpinski, Kuznichenko, Kazakova, Fraze-Frazenko, & Jancarczyk, 2020). The list could go on and on.

When applied to social behavior, 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. Essentially, this work is based on the theory that the agent/influence structure of network behavior can reveal consequential understanding of social behavior. That theory has proved correct, as evidenced by its track record of success in helping us understand how the world works. SCAMP networks however, can provide different understanding.

SCAMP networks are different

People/groups –> Events: Nodes in SCAMP networks do not represent people or groups, but types of events in which agents participate, and are described with active verbs identifying the class or type of agent that can participate in them. Edges do not represent communication or influence. They represent agents’ choices as they travel over events. The direction of travel is based on agents’ choices based on individual preferences, the flow of information that results from their decisions, and their associations with one another. Relationships among event types 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.

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, subject to dynamic levels of urgency and satisfaction. SCAMP constructs goal hierarchies for each agent group. While event nodes are actions in which agents can participate, goal nodes describe states of the world that agents of a given type want to achieve. 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). In the goal hierarchy, measures of “goal satisfaction” propagate from the leaf sub-goals to the root, while measures of “urgency” propagate back down.

Event networks and goal networks are connected: Selected events in an event network are connected to selected goals. The degree of participation in such an event modulates the level of satisfaction of the sub-goals to which it is connected, while the urgency of those sub-goals modulates the attractiveness of events to agents. See the picture for a schematic of a SCAMP model. For the sake of simplicity, it omits differences in types of edge relationships, movement in geospace, “and/or” relationships among goals, and various other technical details that would be part of a fully-fledged SCAMP model.

Events: 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 network nodes (events) such as: 1) election is declared fraudulent, 2) incumbent accepts defeat, 3)  extremist groups merge, 4) political establishment accepts result, 5) supporting and opposing groups clash in city X, 6) clashes spreads, 7) violence decreases,  8) troll farms proliferate, 9) false news about election integrity released, 10) false news about candidate ideology released, and 11) accurate information about election integrity released. 

Goals: A goal hierarchy for say, fact checkers might culminate with “population accepts fact checkers’ assessments” (Goal E), supported by a variety of sub-goals , e.g. Goal A = Fact checkers are able to identify fake news activity, and Goal C = Fact checkers have the capacity to investigate fake news. (We omit goals B and D for brevity.) The interface between events and goals is ” Event 1 <–> Goal A ‘, and  ‘Event 2 <–> Goal B”.  

Knowledge: 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.


Becker, S. O., Hsiao, Y., Pfaff, S., & Rubin, J. (2020). Multiplex Network Ties and the Spatial Diffusion of Radical Innovations: Martin Luther’s Leadership in the Early Reformation. American Sociological Review, 85(5), 857-894. doi:10.1177/0003122420948059
Defense Advanced Research Projects Agency. (2020). Ground Truth. Retrieved from https://www.darpa.mil/program/ground-truth
Dutcher, C. D., Papini, S., Gebhardt, C. S., & Smits, J. A. J. (2021). Network analysis reveals the associations of past quit experiences on current smoking behavior and motivation to quit. Addictive Behaviors, 113. doi:10.1016/j.addbeh.2020.106689
Ferguson, N. (2017). The Square and the Tower: Networks and Power, from the Freemasons to Facebook. New York: Penguin Press.
Goldstone, Robert L., Roberts, M. E., & Gureckis, T. M. (2008). Emergent Processes in Group Behavior. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE, 17(1), 10 – 15.
Gross Thilo, & Blasius, B. (2008). Adaptive coevolutionary networks: a review. Journal of the Royal Society Interface: , 5, 259 – 271. doi:doi:10.1098/rsif.2007.1229
Jovanovski, P., Tomovski, I., & Kocarev, L. (2021). Modeling the spread of multiple contagions on multilayer networks. Physica A: Statistical Mechanics and its Applications, 563. doi:10.1016/j.physa.2020.125410
Karpinski, M., Kuznichenko, S., Kazakova, N., Fraze-Frazenko, O., & Jancarczyk, D. (2020). Geospatial assessment of the territorial road network by fractal method. Future Internet, 12(11), 1-13. doi:10.3390/fi12110201
Parunak, H. V. D., Greanya, J., Morell, J. A., Nadell, S., L., & Sappelsa. (2020). SCAMP’s Stigmergic Model of Social Conflict. Computational and Mathematical Organization Theory. Computational and Mathematical Organization Theory. Retrieved from https://www.abcresearch.org/abc/papers/SBP20SCAMP.pdf.
Reia, S. M. (2020). Diffusion of innovations in Axelrod’s model on small-world networks. International Journal of Modern Physics C, 31(8). doi:10.1142/S0129183120501168

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