Collaborating Authors

Introducing Quantum-Like Influence Diagrams for Violations of the Sure Thing Principle Artificial Intelligence

It is the focus of this work to extend and study the previously proposed quantum-like Bayesian networks to deal with decision-making scenarios by incorporating the notion of maximum expected utility in influence diagrams. The general idea is to take advantage of the quantum interference terms produced in the quantum-like Bayesian Network to influence the probabilities used to compute the expected utility of some action. This way, we are not proposing a new type of expected utility hypothesis. On the contrary, we are keeping it under its classical definition. We are only incorporating it as an extension of a probabilistic graphical model in a compact graphical representation called an influence diagram in which the utility function depends on the probabilistic influences of the quantum-like Bayesian network. Our findings suggest that the proposed quantum-like influence digram can indeed take advantage of the quantum interference effects of quantum-like Bayesian Networks to maximise the utility of a cooperative behaviour in detriment of a fully rational defect behaviour under the prisoner's dilemma game.

QuLBIT: Quantum-Like Bayesian Inference Technologies for Cognition and Decision Artificial Intelligence

This paper provides the foundations of a unified cognitive decision-making framework (QulBIT) which is derived from quantum theory. The main advantage of this framework is that it can cater for paradoxical and irrational human decision making. Although quantum approaches for cognition have demonstrated advantages over classical probabilistic approaches and bounded rationality models, they still lack explanatory power. To address this, we introduce a novel explanatory analysis of the decision-maker's belief space. This is achieved by exploiting quantum interference effects as a way of both quantifying and explaining the decision-maker's uncertainty. We detail the main modules of the unified framework, the explanatory analysis method, and illustrate their application in situations violating the Sure Thing Principle.

Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics Machine Learning

Algorithmic collusion is an emerging concept in current artificial intelligence age. Whether algorithmic collusion is a creditable threat remains as an argument. In this paper, we propose an algorithm which can extort its human rival to collude in a Cournot duopoly competing market. In experiments, we show that, the algorithm can successfully extorted its human rival and gets higher profit in long run, meanwhile the human rival will fully collude with the algorithm. As a result, the social welfare declines rapidly and stably. Both in theory and in experiment, our work confirms that, algorithmic collusion can be a creditable threat. In application, we hope, the frameworks, the algorithm design as well as the experiment environment illustrated in this work, can be an incubator or a test bed for researchers and policymakers to handle the emerging algorithmic collusion.

Uncertainty measurement with belief entropy on interference effect in Quantum-Like Bayesian Networks Artificial Intelligence

Social dilemmas have been regarded as the essence of evolution game theory, in which the prisoner's dilemma game is the most famous metaphor for the problem of cooperation. Recent findings revealed people's behavior violated the Sure Thing Principle in such games. Classic probability methodologies have difficulty explaining the underlying mechanisms of people's behavior. In this paper, a novel quantum-like Bayesian Network was proposed to accommodate the paradoxical phenomenon. The special network can take interference into consideration, which is likely to be an efficient way to describe the underlying mechanism. With the assistance of belief entropy, named as Deng entropy, the paper proposes Belief Distance to render the model practical. Tested with empirical data, the proposed model is proved to be predictable and effective.

Reactive Versus Anticipative Decision Making in a Novel Gift-Giving Game

AAAI Conferences

Evolutionary game theory focuses on the fitness differences between simple discrete or probabilistic strategies to explain the evolution of particular decision-making behavior within strategic situations. Although this approach has provided substantial insights into the presence of fairness or generosity in gift-giving games, it does not fully resolve the question of which cognitive mechanisms are required to produce the choices observed in experiments. One such mechanism that humans have acquired, is the capacity to anticipate. Prior work showed that forward-looking behavior, using a recurrent neural network to model the cognitive mechanism, are essential to produce the actions of human participants in behavioral experiments. In this paper, we evaluate whether this conclusion extends also to gift-giving games, more concretely, to a game that combines the dictator game with a partner selection process. The recurrent neural network model used here for dictators, allows them to reason about a best response to past actions of the receivers (reactive model) or to decide which action will lead to a more successful outcome in the future (anticipatory model). We show for both models the decision dynamics while training, as well as the average behavior. We find that the anticipatory model is the only one capable of accounting for changes in the context of the game, a behavior also observed in experiments, expanding previous conclusions to this more sophisticated game.