arXiv subjects

Han La Poutré

Publications and source records attributed to Han La Poutré.

4 recordsLinked to original sources

arXiv2026

Modelling the complex dynamics of online disinformation cascades

Disinformation on online social media has been on the rise in the past years. While there is extensive literature modelling social cascades, online disinformation cascades show distinctive characteristics: they provoke debunking reactions, they spread over networks with heterogeneous connectivity, and they compete for the limited attention of users --- which recommendation algorithms may partly offset. We propose a model including these ingredients and study it through Monte--Carlo simulations and mean--field approximations, on random graphs and an empirical Twitter network. We find that debunking reactions are double--edged: they reduce the reach of appealing items but allow unappealing ones to reach audiences they would not otherwise reach. Degree heterogeneity makes cascades trigger at a much lower content appeal but limits their final reach: heterogeneous networks are easy to ignite yet hard to saturate. Limited attention cancels the effects from degree heterogeneity, although the mediation of recommendation algorithms may restore them.

physics.soc-ph↗

arXiv2019

Approximate Dynamic Programming with Neural Networks in Linear Discrete Action Spaces

Real-world problems of operations research are typically high-dimensional and combinatorial. Linear programs are generally used to formulate and efficiently solve these large decision problems. However, in multi-period decision problems, we must often compute expected downstream values corresponding to current decisions. When applying stochastic methods to approximate these values, linear programs become restrictive for designing value function approximations (VFAs). In particular, the manual design of a polynomial VFA is challenging. This paper presents an integrated approach for complex optimization problems, focusing on applications in the domain of operations research. It develops a hybrid solution method that combines linear programming and neural networks as part of approximate dynamic programming. Our proposed solution method embeds neural network VFAs into linear decision problems, combining the nonlinear expressive power of neural networks with the efficiency of solving linear programs. As a proof of concept, we perform numerical experiments on a transportation problem. The neural network VFAs consistently outperform polynomial VFAs, with limited design and tuning effort.

cs.LG↗

arXiv2004

Negotiating over Bundles and Prices Using Aggregate Knowledge

Combining two or more items and selling them as one good, a practice called bundling, can be a very effective strategy for reducing the costs of producing, marketing, and selling goods. In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a technique for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining historical sales data, condensed into aggregate knowledge, with current data about the ongoing negotiation process, to exploit these insights. In particular, when negotiating a given bundle of goods with a customer, the shop analyzes the sequence of the customer's offers to determine the progress in the negotiation process. In addition, it uses aggregate knowledge concerning customers' valuations of goods in general. We show how the shop can use these two sources of data to locate promising alternatives to the current bundle. When the current negotiation's progress slows down, the shop may suggest the most promising of those alternatives and, depending on the customer's response, continue negotiating about the alternative bundle, or propose another alternative. Extensive computer simulation experiments show that our approach increases the speed with which deals are reached, as well as the number and quality of the deals reached, as compared to a benchmark. In addition, we show that the performance of our system is robust to a variety of changes in the negotiation strategies employed by the customers.

cs.MA↗

arXiv2004

Online Learning of Aggregate Knowledge about Non-linear Preferences Applied to Negotiating Prices and Bundles

In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences with current data about the ongoing negotiation process. The developed procedure either works with already obtained aggregate knowledge or, in the absence of such knowledge, learns the relevant information online. We conduct computer experiments with simulated customers that have_nonlinear_ preferences. We show how, for various types of customers, with distinct negotiation heuristics, our procedure (with and without the necessary aggregate knowledge) increases the speed with which deals are reached, as well as the number and the Pareto efficiency of the deals reached compared to a benchmark.

cs.MA↗