MORE: Merged Opinions Reputation Model

TL;DR

This paper introduces MORE, a probabilistic reputation model that accounts for opinion decay, source reliability, and certainty, adaptable to explicit and implicit opinions, and applied to predict football team behavior.

Contribution

The paper presents a novel reputation model that integrates temporal decay, source impact, and certainty, with a flexible approach for extracting opinions from behavior, demonstrated in football.

Findings

The model effectively predicts football team behavior.

It handles both explicit and implicit opinions.

The approach improves reputation assessment accuracy.

Abstract

Reputation is generally defined as the opinion of a group on an aspect of a thing. This paper presents a reputation model that follows a probabilistic modelling of opinions based on three main concepts: (1) the value of an opinion decays with time, (2) the reputation of the opinion source impacts the reliability of the opinion, and (3) the certainty of the opinion impacts its weight with respect to other opinions. Furthermore, the model is flexible with its opinion sources: it may use explicit opinions or implicit opinions that can be extracted from agent behavior in domains where explicit opinions are sparse. We illustrate the latter with an approach to extract opinions from behavioral information in the sports domain, focusing on football in particular. One of the uses of a reputation model is predicting behavior. We take up the challenge of predicting the behavior of football teams in football matches, which we argue is a very interesting yet difficult approach for evaluating the model.