Modeling decision making for IARPA

IARPA, the whackier younger sibling of the government’s wild idea incubator DARPA, wanted to understand the hidden intentions of decision makers. To that end, we delivered a computational model of decision making in Java. By building a model, we enabled users to: (1) perturb the system to estimate the robustness of an outcome, (2) experiment with levers of influence. The model integrated with a complex set of other related systems built by other contractors.

I learned a lot of decision theory as part of implementing this model. My main takeaways are:

  • Individuals’ decisions can be represented as “decision matrices” (interests by available choices, with weights in each cell). We often want the interests to be ranked. Weighting choices ordinally with ties allowed within each interest is often easiest.
  • You can influence an outcome by influencing the decision-making environment. For instance, adding more interests can change outcomes. Changing who is consulted can also change the outcome.
  • Decision matrices can be turned into decisions through multiple techniques, including: (1) maximizing expected utility, (2) eliminating the worst options on the most important dimension (“elimination by aspects”), (3) choosing the best option on the most important dimension (“lexicographic decision heuristic”), (4) and maximin (maximizing the worst-case payoff).
  • The maximin heuristic is very risk averse. People tend to use it when the decision is especially difficult.
  • Social influence and power relationships can weaken/strengthen/change people’s interests and decision matrix weights.
  • The group’s style of decision making (a la the Vroom-Yetton model) informs how individual choices are aggregated.

Using Java professionally (and for the strange bedfellow of modeling) meant a lot of learning for me too. (Once burned by a boxed variable, never again.)

Using social network analysis to anticipate rare events

My boss Elisa Bienenstock and I wrote a white paper on how Social Network Analysis can help forecast and detect rare events. It appears in Anticipating Rare Events: Can Acts of Terror, Use of Weapons of Mass Destruction or Other High Profile Acts Be Anticipated? A Scientific Perspective on Problems, Pitfalls and Prospective Solutions (N. Chesser, Ed.), which is an interdisciplinary review for operators in terrorism prevention within DoD/DHS/USG agencies.

In our paper, we focus on two insights from the field of social network analysis (SNA). First, innovation tends to happen at the periphery of social networks, rather than in a network’s core. New behaviors, insights, and events are more likely to occur when people with different backgrounds mix. Second, when a novel event involving new participants is being planned, we can observe signs of that activity in the network. New regions of the network will become active and new substructures will emerge. We conclude that an SNA-based approach for anticipating terrorism and other rare events would watch for two changes in structure: (1) new ties at the edges of networks, and (2) the new involvement of individuals with particular talents and resources with each other.