Optimal Blends of History and Intelligence for Robust Antiterrorism Policy

Abstract Antiterrorism analysis requires that security agencies blend evidence on historical patterns of terrorist behavior with incomplete intelligence on terrorist adversaries to predict possible terrorist operations and devise appropriate countermeasures. We model interactions between reactive, adaptive and intelligent adversaries embedded in minimally sufficient organizational settings to study the optimal analytic mixture, expressed as historical memory reach-back and the number of anticipatory scenarios, that should be used to design antiterrorism policy. We show that history is a valuable source of information when the terrorist organization evolves and acquires new capabilities at such a rapid pace that makes optimal strategies advocated by game-theoretic reasoning unlikely to succeed

Axelrod’s metanorm games on networks

Metanorms is a mechanism proposed to promote cooperation in social dilemmas. Recent experimental results show that network structures that underlie social interactions influence the emergence of norms that promote cooperation. We generalize Axelrod’s analysis of metanorms dynamics to interactions unfolding on networks through simulation and mathematical modeling. Network topology strongly influences the effectiveness of the metanorms mechanism in establishing cooperation. In particular, we find that average degree, clustering coefficient and the average number of triplets per node play key roles in sustaining or collapsing cooperationSpanish MICINN projects CSD2010-00034 (CONSOLIDER-INGENIO 2010) and DPI2010-16920, and by the Junta de Castilla y Leo´ n, references BU034A08 and GREX251-2009

Proportion of time spent in the emergence and collapse zones.

<p>Proportion of time that the simulation spends in the norm collapse and emergence zones as a function of key network statistics using similar projections as those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020474#pone-0020474-g005" target="_blank">Figure 5</a>. Color codes the fraction of simulation time spent in each zone computed for each bin. Time spent outside either zone is insignificant.</p

Legend for gradient maps.

<p>The map applies to both analytic and simulated gradient landscapes. The axes represent the average boldness and vengefulness of the population as its strategic characteristics. For each point, we measure the direction and speed of population drift. For analytical landscapes, we will also pinpoint the expected location of the evolutionary stable states. For simulation landscapes, we will be measuring the time that the simulation spends in each of the two key regions: norm emergence and norm collapse zones. Sample maps for different network topologies are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0020474#pone-0020474-g007" target="_blank">Figure 7</a>.</p

Minimal interconnectedness necessary for a cooperative evolutionary stable state.

<p>Minimal interconnectedness necessary for a cooperative evolutionary stable state to exist in the simplified analytical model for any given average degree of the network, compared to the expected interconnectedness of different network topologies with radius 1. Default metanorms parameters are assumed.</p

Examples of methods of solving social dilemmas based on Kollock's ontology [11].

<p>Solutions to social dilemmas can be classified as motivational, strategic or structural depending on whether players are assumed egoist and whether the rules of the game can be changed.</p