The goal programming (GP) is a well-known approach applied to multi-criteria decision making (M-DM). It has been used in many domains and the literature offers diverse extensions of this procedure. On the other hand, so far, some evident analogies between M-DM under certainty and scenario-based...

This paper studies sequential information acquisition by an ambiguity-averse decision maker (DM), who decides how long to collect information before taking an irreversible action. The agent optimizes against the worst-case belief and updates prior by prior. We show that the consideration of...

We study the effect of ambiguity on timing decisions. An agent faces a stopping problem with an uncertain stopping payoff and a stochastic time limit. The agent is unsure about the correct model quantifying the uncertainty and seeks to maximize her payoff guarantee over a set of plausible...

This paper proposes a new approach for modeling investor fear after rare disasters. The key element is to take into account that investors' information about fundamentals driving rare downward jumps in the dividend process is not perfect. Bayesian learning implies that beliefs about the...

We consider a class of multi-armed bandit problem which is at the same time an arm-acquiring, restless and mortal bandit, and where the rewards follow any distribution. This is the case for a committee whose mission is to select the best element of a set of talents who live for K periods, and...

This paper explores the value of memory in decision making in dynamic environments. We examine the decision problem faced by an agent with bounded memory who receives a sequence of signals from a partially observable Markov decision process. We characterize environments in which the optimal...

Unknown model parameters, like expected returns, cannot be accurately estimated from short samples. Respective estimation error most likely leads to the portfolio, inconsistent with its target risk/return profile. We investigate the ways of reducing the impact of estimation error on portfolio...

Starting from the Avellaneda–Stoikov framework, we consider a market maker who wants to optimally set bid/ask quotes over a finite time horizon, to maximize her expected utility. The intensities of the orders she receives depend not only on the spreads she quotes, but also on unobservable...

The main result of this paper is that, in continuous time games with imperfect monitoring it is better to average information over time rather than respond at every instant. The two main reasons why it is better to introduce delayed response to signals are that it helps to (1) loosen...