Материал: искусственный интеллект

Table 4. Comparison between the Quantum Prospect Decision Theory (DT) [22], the dynamic heuristic (DH) [18] and the law of maximal uncertainty (MU) of the balanced quantum-like model. The results of the dynamic heuristic (DH) and the law of maximal uncertainty (MU) are similar, however the law of maximal uncertainty (MU) was not adapted to a domain.

4 Conclusion

Physical experiments indicate that wave functions are present in the world [21]. They state that the size does not matter and that a very large number of atoms can be entangled [1, 9]. Clues from psychology also indicate that human cognition is based on quantum probability rather than the traditional probability theory as explained by Kolmogorov’s axioms [5–8]. This approach could lead to the conclusion that a wave function can be present at the macro scale of our daily life.

We introduce a balanced Bayesian quantum-like model that is based on probability waves. The law of maximum uncertainty indicates how to choose a possible phase value of the wave resulting in a meaningful probability value. The law of maximal uncertainty of the balanced quantum-like model is not static, meaningful and does not need to be adapted to a specific domain. The results obtained show that the model can make predictions regarding human decisionmaking with a meaningful interpretation.

Acknowledgment. This work was supported by national funds through Funda¸c˜ao para a Ciˆencia e a Tecnologia (FCT) with reference UID/CEC/50021/2013. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Introducing Quantum-Like Influence

Diagrams for Violations of the Sure

Thing Principle

Catarina Moreira1(B) and Andreas Wichert2

1 School of Business, University of Leicester,

University Road, Leicester LE1 7RH, UK

cam74@le.ac.uk

2 Instituto Superior T´ecnico, INESC-ID,

Av. Professor Cavaco Silva, 2744-016 Porto Salvo, Portugal

andreas.wichert@tecnico.ulisboa.pt

Abstract. It is the focus of this work to extend and study the previously proposed quantum-like Bayesian networks (Moreira and Wichert, 2014, 2016) 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 diagram can indeed take advantage of the quantum interference e ects 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.

Keywords: Quantum cognition · Quantum-like influence diagrams · Quantum-Like Bayesian Networks

1 Introduction

In this work, we extend the Quantum-Like Bayesian Network previously proposed by Moreira and Wichert (2014, 2016) by incorporating the framework of expected utility. This extension is motivated by the fact that quantum-like models tend to explain the probability distributions in several decision scenarios where the agent (or the decision-maker) tends to act irrationally (Busemeyer and Bruza 2012; Bruza et al. 2015). By irrational, we mean that an individual

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B. Coecke and A. Lambert-Mogiliansky (Eds.): QI 2018, LNCS 11690, pp. 91–108, 2019. https://doi.org/10.1007/978-3-030-35895-2_7

92 C. Moreira and A. Wichert

chooses strategies that do not maximise or violate the axioms of expected utility. It is not enough to know these probability distributions. On the contrary, it would be desirable to use this probabilistic information to help us act upon a real world decision scenario. For instance, if a patient has cancer, it is not enough for a doctor to know the probability distribution of success of di erent treatments. The doctor needs to act and choose a treatment based on specific information about the patient and how this treatment will a ect him/her. Decision-making models such as the expected utility hypothesis are used to decide how to act in the world. The main problem with such decision-making models is that it is very challenging to determine the right action in a decision task where the outcomes of the actions are not fully determined (Koller and Friedman 2009). For this reason, we suggest to extend the previously proposed Quantum-Like Bayesian Network to a Quantum-Like Influence diagram where we take into account both the quantum-like probabilities (incorporating quantum interference e ects) of the various outcomes and the preferences of an individual between these outcomes.

Generally speaking, an Influence diagram is a compact directed acyclical graphical representation of a decision scenario originally proposed by Howard and Matheson (1984) which consists in three types of nodes: random variables (nodes) of a Bayesian Network, action nodes representing a decision that we need to make, and an utility function. The goal is to make a decision, which maximises the expected utility function by taking into account probabilistic inferences performed on the Bayesian Network. However, since influence diagrams are based on classical Bayesian Networks, then they cannot cope with the paradoxical findings reported over the literature.

It is the focus of this work to study the implications of incorporating Quantum-Like Bayesian Networks in the context of influence graphs. By doing so, we are introducing quantum interference e ects that can disturb the final probability outcomes of a set of actions and a ect the final expected utility. We will study how one can use influence diagrams to explain the paradoxical findings of the prisoner’s dilemma game based on expected utilities.

2Revisiting the Prisoner’s Dilemma and the Expected Utility Hypothesis

The Prisoner’s Dilemma game consists in two players who are in two separate confinements with no means of communicating with each other. They were o ered a deal: if one defects against the other, he is set free while the other gets a heavy charge. If they both defect, they get both a big charge and if they both cooperate by remaining silent, they get a small charge. Figure 1 shows an example of a payo matrix for the Prisoner’s Dilemma used in the experiments of Shafir and Tversky (1992) where the goal is to score the maximum number of points.

Looking at the payo matrix, one can see that the best action for both players is to cooperate, however experimental findings show that the majority of the

Fig. 1. Example of a payo matrix used in the Shafir and Tversky (1992) Prisoner’s Dilemma experiment

players choose to def ect even when it is known that the other player chose to cooperate. The Prisoner’s Dilemma is a clear example of how two perfectly rational individuals choose to defect (they prefer an individual reward), rather than choosing the option which is best for both (to cooperate). The expected utility hypothesis is a framework that enables us to explain why this happens.

The expected utility hypothesis corresponds to a function designed to take into account decisions under risk. It consists of a choice of a possible set of actions represented by a probability distribution over a set of possible payo s (von Neumann and Morgenstern 1953). It is given by Eq. 1,

where U (xi) is an utility function associated to event xi.

In the experiment of Shafir and Tversky (1992), the participant needed to choose between de actions def ect or cooperate. We will address to this participant as player 2, P 2, and his opponent, to player 1, P 1. According to the expected utility hypothesis, P 2 would have to choose the action that would grant him the highest expected utility. Assuming that we do not know what P 1 chose (so we model this with a neutral prior of 0.5), we can compute the expected utility of Player 2 as

EU [Def ect] = 0.5 × U (P 1 = D, P 2 = D) + 0.5 × U (P 1 = C, P 2 = D) = 57.5,

EU [Cooperate] = 0.5 × U (P 1 = D, P 2 = C) + 0.5 × U (P 1 = C, P 2 = C) = 50.

Note that U (P 1 = x, P 2 = y) corresponds to the utility of player 1 choosing action x and player 2 choosing action y. The calculations show that the action that maximises the player’s expected utility is Def ect. This is what it is known as the Maximum Expected Utility hypothesis (MEU).

In the end of the 70’s, Daniel Kahneman and Amos Tversky showed in a set of experiments that in many real life situations, the predictions of the expected utility were completely inaccurate (Tversky and Kahneman 1974; Kahneman et al. 1982; Kahneman and Tversky 1979). This means that a decision theory should be predictive in the sense that it should say what people actually do choose, instead of what they must choose. The Prisoner’s Dilemma game is one of the experiments that show the inaccuracy of the expected utility hypothesis by