Decision making with decision event graphs

We introduce a new modelling representation, the Decision Event Graph (DEG), for asymmetric multistage decision problems. The DEG explicitly encodes conditional independences and has additional significant advantages over other representations of asymmetric decision problems. The colouring of edges makes it possible to identify conditional independences on decision trees, and these coloured trees serve as a basis for the construction of the DEG. We provide an efficient backward-induction algorithm for finding optimal decision rules on DEGs, and work through an example showing the efficacy of these graphs. Simplifications of the topology of a DEG admit analogues to the sufficiency principle and barren node deletion steps used with influence diagrams

Adversarial Risk Analysis: The Somali Pirates case

Some of the current world’s biggest problems revolve around security issues. This has raised recent interest in resource allocation models to manage security threats, from terrorism to organized crime through money laundering. One of those approaches is adversarial risk analysis, which aims at dealing with decision making problems with intelligent opponents and uncertain outcomes. We show here how such framework may cope with a current important security issue in relation with piracy in the Somali coasts. Specifically, we describe how to support the owner of a ship in managing risks from piracy in that area. We illustrate how a sequential defend-attack-defend model can be used to formulate this decision problem and solve it for the ship owner. Emphasis will be put on explaining how we can model the Pirates thinking in order to anticipate their behavior and how it would lead to a predictive probability distribution, from the defender’s perspective, over what the Pirates may do

Copmment on Egalitarianism under Incomplete Information

The paper aims at extending the egalitarian principle to environments with incomplete information. The approach is primarily axiomatic, focusing on the characteristic property of monotonicity: no member of the society should be worse off when more collective decisions are available. I start by showing the incompat- ibility of this property with incentive efficiency, even in quasi-linear environments. This serious impossibility result does not follow from the mere presence of incentive constraints, but instead from the fact that information is incomplete (asymmetric information at the time of making a decision). I then weaken the monotonicity property so as to require it only when starting from incentive compatible mecha- nisms at which interim utilities are transferable (in a weak sense). Adding other axioms in the spirit of Kalai's (Econometrica, 1977, Theorem 1) classical character- ization of the egalitarian principle under complete information, I obtain a partial characterization of a natural extension of the lex-min solution to problems with incomplete information. Next, I prove that, in each social choice problem, there is a unique way of rescaling the participants' interim utilities so as to make this solu- tion compatible with the ex-ante utilitarian principle. These two criteria coincides in the rescaled utilities exactly at the incentive ecient mechanisms that maxi- mize Harsanyi and Selten's (Management Science, 1972) weighted Nash product. These concepts are illustrated on classical examples of profit-sharing, public good production and bilateral trade. The richness of the topic of social choice under in- complete information is illustrated by considering two alternative extensions of the egalitarian principle { one based on an idea of equity from the point of view of the individuals themselves (given their private information) instead of an uninformed third party (social planner or arbitrator), and another notion based on the idea of