Classification methods from machine learning are receiving a lot of attention in the transportation modelling community. This is motivated by the access to large databases, and to various success stories reported in this research community. Discrete choice ...
In many daily tasks, we make multiple decisions before reaching a goal. In order to learn such sequences of decisions, a mechanism to link earlier actions to later reward is necessary. Reinforcement learning (RL) theory suggests two classes of algorithms s ...
When faced with a decision, most people like to know the odds and prefer to avoid ambiguity. It has been suggested that this aversion to ambiguity is linked to people's assumption of worst possible outcomes. We used two closely linked behavioural tasks in ...
Dynamic decision-making under uncertainty has a long and distinguished history in operations research. Due to the curse of dimensionality, solution schemes that naively partition or discretize the support of the random problem parameters are limited to sma ...
In standard analyses of Swiss energy and climate policies, the speed and extent of energy efficiency improvements (EEI) are usually assumed to be unaffected, even by policies designed to foster innovation. This project introduces endogenous EEI and barrier ...
The central task in many interactive machine learning systems can be formalized as the sequential optimization of a black-box function. Bayesian optimization (BO) is a powerful model-based framework for \emph{adaptive} experimentation, where the primary go ...
Fatigue safety verification of existing bridges that uses ‘‘re-calculation’’ based on codes, usually results in insufficient fatigue safety, triggering invasive interventions. Instead of “re-calculation”, Structural Health Monitoring (SHM) should be used f ...
We use homeostasis, the maintenance of steady states in an organism, to explain some of the decisions made by participants in a business process. We use Vickers’ Appreciative System to model the homeostatic states with Harel’s statecharts. We take the exam ...
Modern applications accumulate data at an exponentially increasing rate and traditional database systems struggle to keep up.
Decision support systems used in industry, rely heavily on data analysis, and require real-time responses irrespective of data siz ...
We study stochastic bilevel programs where the leader chooses a binary here-and-now decision and the follower responds with a continuous wait-and-see-decision. Using modern decision rule approximations, we construct lower bounds on an optimistic version an ...
