Models can be wrong and recognising their limitations is important in financial and economic decision making under uncertainty. Robust strategies, which are least sensitive to perturbations of the underlying model, take uncertainty into account. Finding the explicit set of alternative models...

We can overcome uncertainty with uncertainty. Using randomness in our choices and in what we control, and hence in the decision making process, could potentially offset the uncertainty inherent in the environment and yield better outcomes. The example we develop in greater detail is the...

This paper is about the issue of input parameter uncertainty in portfolio optimization in a discrete setting with finite states (such as the case in a world with different macroeconomic regimes). In such a setting, being unable to assign reliable point estimates to the probabilities (or...

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...

We can overcome uncertainty with uncertainty. Using randomness in our choices and in what we control and hence in the decision making process, could potentially offset the uncertainty inherent in the environment and yield better outcomes. This methodology is suitable for the social sciences...

Economists often estimate a subset of their model parameters outside the model and let the decision-makers inside the model treat these point estimates as-if they are correct. This practice ignores model ambiguity, opens the door for misspecification of the decision problem, and leads to...

Economists often estimate economic models on data and use the point estimates as a stand-in for the truth when studying the model’s implications for optimal decision-making. This practice ignores model ambiguity, exposes the decision problem to misspecification, and ultimately leads to...

In typical robust portfolio selection problems, one mainly finds portfolios with the worst-case return under a given uncertainty set, in which asset returns can be realized. A too large uncertainty set will lead to a too conservative robust portfolio. However, if the given uncertainty set is not...

We study how to optimally match agents in a dynamic market with heterogeneous match values. A network topology determines the feasible matches in the market. We consider networks that are two-sided when all matches include two agents, or acyclic otherwise. An inherent trade-off arises between...