Outputs (7)

Elicitation for decision problems under severe uncertainties (2024)
Presentation / Conference Contribution
Nakharutai, N., Troffaes, M., & Destercke, S. (2024, November). Elicitation for decision problems under severe uncertainties. Presented at The 16th International Conference on Scalable Uncertainty Management (SUM 2024), Palermo, Italy

In this paper, we investigate the problem of eliciting information from an expert, where the assumed uncertainty model is a coherent upper prevision (or equivalently a closed convex set of probabilities). The goal is to solve a decision problem under... Read More about Elicitation for decision problems under severe uncertainties.

Regret-based budgeted decision rules under severe uncertainty (2024)
Journal Article
Nakharutai, N., Destercke, S., & Troffaes, M. C. M. (2024). Regret-based budgeted decision rules under severe uncertainty. Information Sciences, 665, Article 120361. https://doi.org/10.1016/j.ins.2024.120361

One way to make decisions under uncertainty is to select an optimal option from a possible range of options, by maximizing the expected utilities derived from a probability model. However, under severe uncertainty, identifying preci... Read More about Regret-based budgeted decision rules under severe uncertainty.

Improving and benchmarking of algorithms for decision making with lower previsions (2019)
Journal Article
Nakharutai, N., Troffaes, M. C., & Caiado, C. (2019). Improving and benchmarking of algorithms for decision making with lower previsions. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 113, 91-105. https://doi.org/10.1016/j.ijar.2019.06.008

Maximality, interval dominance, and E-admissibility are three well-known criteria for decision making under severe uncertainty using lower previsions. We present a new fast algorithm for nding maximal gambles. We compare its performance to existing a... Read More about Improving and benchmarking of algorithms for decision making with lower previsions.

Improving and benchmarking of algorithms for decision making with lower previsions (2019)
Presentation / Conference Contribution
Nakharutai, N., Troffaes, M. C., & Caiado, C. (2019, July). Improving and benchmarking of algorithms for decision making with lower previsions. Paper presented at ISIPTA 2019, Ghent, Belgium

Evaluating betting odds and free coupons using desirability (2019)
Journal Article
Nakharutai, N., Caiado, C. C., & Troffaes, M. C. (2019). Evaluating betting odds and free coupons using desirability. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 106, 128-145. https://doi.org/10.1016/j.ijar.2019.01.002

In the UK betting market, bookmakers often offer a free coupon to new customers. These free coupons allow the customer to place extra bets, at lower risk, in combination with the usual betting odds. We are interested in whether a customer can exploit... Read More about Evaluating betting odds and free coupons using desirability.

Improved linear programming methods for checking avoiding sure loss (2018)
Journal Article
Nakharutai, N., Troffaes, M. C., & Caiado, C. C. (2018). Improved linear programming methods for checking avoiding sure loss. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 101, 293-310. https://doi.org/10.1016/j.ijar.2018.07.013

We review the simplex method and two interior-point methods (the affine scaling and the primal-dual) for solving linear programming problems for checking avoiding sure loss, and propose novel improvements. We exploit the structure of these problems t... Read More about Improved linear programming methods for checking avoiding sure loss.

Efficient algorithms for checking avoiding sure loss (2017)
Presentation / Conference Contribution
Nakharutai, N., Troffaes, M. C., & Caiado, C. C. (2017, July). Efficient algorithms for checking avoiding sure loss. Presented at The Tenth International Symposium on Imprecise Probability: Theories and Applications (ISIPTA ’17), Lugano, Switzerland

Sets of desirable gambles provide a general representation of uncertainty which can handle partial information in a more robust way than precise probabilities. Here we study the effectiveness of linear programming algorithms for determining whether o... Read More about Efficient algorithms for checking avoiding sure loss.