Decision Modeling in Markovian Multi-Agent Systems

In this paper, we model a decision-making process involving a set of interacting agents. We use Markovian opinion dynamics, where each agent switches between decisions according to a continuous time Markov chain. Existing opinion dynamics models are extended by introducing attractive and repulsive forces that act within and between groups of agents, respectively. Such an extension enables the resemblance of behaviours emerging in networks where agents make decisions that depend both on their own preferences and the decisions of specific groups of surrounding agents. The considered modeling problem and the contributions in this paper are inspired by the interaction among road users (RUs) at traffic junctions, where each RU has to decide whether to go or to yield

Collective Decision Making using Attractive and Repulsive Forces in Markovian Opinion Dynamics

In this paper, we model a decision-making process involving a set of interacting agents. We use Markovian opinion dynamics, where each agent switches between decisions according to a continuous time Markov chain. Existing opinion dynamics models are extended by introducing attractive and repulsive forces that act within and between groups of agents, respectively. Such an extension enables the resemblance of behaviours emerging in networks where agents make decisions that depend both on their own preferences and the decisions of specific groups of surrounding agents. The considered modeling problem and the contributions in this paper are inspired by the interaction among road users (RUs) at traffic junctions, where each RU has to decide whether to go or to yield

Decision Modeling in Markovian Multi-Agent Systems

In this paper, we model a decision-making process involving a set of interacting agents. We use Markovian opinion dynamics, where each agent switches between decisions according to a continuous time Markov chain. Existing opinion dynamics models are extended by introducing attractive and repulsive forces that act within and between groups of agents, respectively. Such an extension enables the resemblance of behaviours emerging in networks where agents make decisions that depend both on their own preferences and the decisions of specific groups of surrounding agents. The considered modeling problem and the contributions in this paper are inspired by the interaction among road users (RUs) at traffic junctions, where each RU has to decide whether to go or to yield

Repulsive Markovian models for opinion dynamics

We consider the problem of modeling a decision-making process in a network of stochastic agents, each described as a Markov chain. Two approaches for describing disagreement among agents as social forces are studied. These forces modulate the rates at which agents transition between decisions. We define similarity conditions between the two disagreement models and derive a method for obtaining two model instances that fulfill this property. Moreover, we show that a condition for significantly reducing the state-space dimension through marginalization can be derived for both models. However, using a counterexample, we also demonstrate that similarity is not generally possible for models that can be marginalized. Finally, we recommend which disagreement model to use based on the results of our compariso

Collective Decision Making using Attractive and Repulsive Forces in Markovian Opinion Dynamics

In this paper, we model a decision-making process involving a set of interacting agents. We use Markovian opinion dynamics, where each agent switches between decisions according to a continuous time Markov chain. Existing opinion dynamics models are extended by introducing attractive and repulsive forces that act within and between groups of agents, respectively. Such an extension enables the resemblance of behaviours emerging in networks where agents make decisions that depend both on their own preferences and the decisions of specific groups of surrounding agents. The considered modeling problem and the contributions in this paper are inspired by the interaction among road users (RUs) at traffic junctions, where each RU has to decide whether to go or to yield.Comment: Revised version of our original submission. Major changes include a new example application throughout the paper, which consists of a Yield/Go state traffic intersection problem, a reformulation of the repulsive force function, an updated derivation of the marginalized model and a results section that considers the Yield/Go intersection proble

Modelling Traffic Scenarios via Markovian Opinion Dynamics

We address the question of whether opinion dynamics models can be exploited in novel scenarios, such as traffic flow on highway lanes. In this paper, we design a Markovian model and compare its predictions with those obtained from the widely recognized Cell Transmission Model (CTM) for the same traffic scenario. We identify potential challenges that may arise and propose strategies to address them. Furthermore, we present a concise demonstration showcasing the predictive capabilities of our proposed model through a small-scale exampl