Focality and Asymmetry in Multi-battle Contests

This article examines behavior in two-person constant-sum Colonel Blotto games in which each player maximizes the expected total value of the battlefields won. A lottery contest success function is employed in each battlefield. Recent experimental research on such games provides only partial support for Nash equilibrium behavior. We hypothesize that the salience of battlefields affects strategic behavior (the salient target hypothesis). We present a controlled test of this hypothesis – against Nash predictions – when the sources of salience come from certain asymmetries in either battlefield values or labels (as in Schelling (1960)). In both cases, subjects over-allocate the resource to the salient battlefields relative to the Nash prediction. However, the effect is stronger with salient values. In the absence of salience, we replicate previous results in the literature supporting the Nash prediction

Principles of Security and Trust: 7th International Conference, POST 2018, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018, Thessaloniki, Greece, April 14-20, 2018, Proceedings

authentication; computer science; computer software selection and evaluation; cryptography; data privacy; formal logic; formal methods; formal specification; internet; privacy; program compilers; programming languages; security analysis; security systems; semantics; separation logic; software engineering; specifications; verification; world wide we

Protecting infrastructure networks from disinformation

Massive amount of information shared on online platforms makes the verification of contents time-consuming. Concern arises when the misleading or false information, called "disinformation", is exposed to many online platform users who have potential to react on it. The spread of disinformation can cause malicious consequences such as damage to critical infrastructure networks such as electric power, gas, and water distribution networks. Imagine a fake electricity discount, shared by disinformation campaigns, is exposed to many users on Twitter encouraging them to shift their electricity usage to a specific peak hour. If the population of users who engage with the fake discount exceeds a threshold, a blackout can happen due to the overconsumption of electricity. Thus, users access and exposure to accurate information on time can reinforce the infrastructures which are backbone of well-being for societies and economic growth. In this dissertation, we propose solutions to protect infrastructure networks from disinformation campaigns. The solutions include: (i) an integrated epidemiological-optimization (EPO) model involving a mixed integer linear programming model (MIP) and SIR (Susceptible, Infected, Recovered) model to protect physical infrastructure networks by counter disinformation (accurate information) spread in information networks, (ii) a disinformation interdiction model to influence physical infrastructure commodity consumers with accurate information given the topology of social network, (ii) a robust mixed integer linear programming model to propose solutions superior to the original EPO model under uncertain spread of disinformation scenarios. We illustrate our proposed models with two different case studies: (i) a sub-network of the western US interconnection power grid located in Los Angeles County in California, and (ii) the New York City subway system