Psychological evidence reveals the influence of personality traits on decision-making. For instance, agreeableness is generally associated with positive outcomes in negotiations, whereas neuroticism is often linked to less favorable outcomes. This paper introduces a simulation framework centered on Large Language Model (LLM) agents endowed with synthesized personality traits. The agents negotiate within bargaining domains and possess customizable personalities and objectives. The experimental results show that the behavioral tendencies of LLM-based simulations could reproduce behavioral patterns observed in human negotiations. The contribution is twofold. First, we propose a simulation methodology that investigates the alignment between the linguistic and economic capabilities of LLM agents. Secondly, we offer empirical insights into the strategic impact of Big-Five personality traits on the outcomes of bilateral negotiations. We also provide a case study based on synthesized bargaining dialogues to reveal intriguing behaviors, including deceitful and compromising behaviors.

Community Question Answering (CQA) sites have spread and multiplied significantly in recent years. Sites like Reddit, Quora, and Stack Exchange are becoming popular amongst people interested in finding answers to diverse questions. One practical way of finding such answers is automatically predicting the best candidate given existing answers and comments. Many studies were conducted on answer prediction in CQA but with limited focus on using the background information of the questionnaires. We address this limitation using a novel method for predicting the best answers using the questioner's background information and other features, such as the textual content or the relationships with other participants. Our answer classification model was trained using the Stack Exchange dataset and validated using the Area Under the Curve (AUC) metric. The experimental results show that the proposed method complements previous methods by pointing out the importance of the relationships between users, particularly throughout the level of involvement in different communities on Stack Exchange. Furthermore, we point out that there is little overlap between user-relation information and the information represented by the shallow text features and the meta-features, such as time differences.

Artificial Intelligence (AI) has long pursued models, theories, and techniques to imbue machines with human-like general intelligence. Yet even the currently predominant data-driven approaches in AI seem to be lacking humans' unique ability to solve wide ranges of problems. This situation begs the question of the existence of principles that underlie general problem-solving capabilities. We approach this question through the mathematical formulation of analogies across different problems and solutions. We focus in particular on problems that could be represented as tree-like structures. Most importantly, we adopt a category-theoretic approach in formalising tree problems as categories, and in proving the existence of equivalences across apparently unrelated problem domains. We prove the existence of a functor between the category of tree problems and the category of solutions. We also provide a weaker version of the functor by quantifying equivalences of problem categories using a metric on tree problems.

The enactive approach to cognition is typically proposed as a viable alternative to traditional cognitive science. Enactive cognition displaces the explanatory focus from the internal representations of the agent to the direct sensorimotor interaction with its environment. In this paper, we investigate enactive learning through means of artificial agent simulations. We compare the performances of the enactive agent to an agent operating on classical reinforcement learning in foraging tasks within maze environments. The characteristics of the agents are analysed in terms of the accessibility of the environmental states, goals, and exploration/exploitation tradeoffs. We confirm that the enactive agent can successfully interact with its environment and learn to avoid unfavourable interactions using intrinsically defined goals. The performance of the enactive agent is shown to be limited by the number of affordable actions.

There has been a great deal of interest about negotiations having interdependent issues and nonlinear utility spaces as they arise in many realistic situations. In this case, reaching a consensus among agents becomes more difficult as the search space and the complexity of the problem grow. Nevertheless, none of the proposed approaches tries to quantitatively assess the complexity of the scenarios in hand, or to exploit the topology of the utility space necessary to concretely tackle the complexity and the scaling issues. We address these points by adopting a representation that allows a modular decomposition of the issues and constraints by mapping the utility space into an issue-constraint hypergraph. Exploring the utility space reduces then to a message passing mechanism along the hyperedges by means of utility propagation. Adopting such representation paradigm will allow us to rigorously show how complexity arises in nonlinear scenarios. To this end, we use the concept of information entropy in order to measure the complexity of the hypergraph. Being able to assess complexity allows us to improve the message passing algorithm by adopting a low-complexity propagation scheme. We evaluated our model using parametrized random hyper- graphs, showing that it can optimally handle complex utility spaces while outperforming previous sampling approaches.

Decision making has proposed multiple methods to help the decision maker in his analysis, by suggesting ways of formalization of the preferences as well as the assessment of the uncertainties. Although these techniques are established and proven to be mathematically sound, experience has shown that in certain situations we tend to avoid the formal approach by acting intuitively. Especially, when the decision involves a large number of attributes and outcomes, and where we need to use pragmatic and heuristic simplifications such as considering only the most important attributes and omitting the others. In this paper, we provide a model for decision making in situations subject to a large predictive uncertainty with a small learning sample. The high predictive uncertainty is concretized by a countably infinite number of prospects, making the preferences assessment more difficult. Our main result is an extension of the Maximum Entropy utility (MEU) principle into an asymptotic maximum entropy utility principle for preferences elicitation. This will allow us to overcome the limits of the existing MEU method to the extend that we focus on utility assessment when the set of the available discrete prospects is countably infinite. Furthermore, our proposed model can be used to analyze situations of high-cognitive load as well as to understand how humans handle these problems under Ceteris Paribus assumption.