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quantum machine learning

20 M. Bitbol

(according to which “physics is to be regarded not so much as the study of something a priori given, but rather as the development of methods of ordering and surveying human experience” [5]) can now avail itself of its concretization in a multiplicity of domains whose isomorphism reveals a common epistemological approach towards what is still called, by habit, their “object”.

The fact that quantum theory apply so well to many human sciences then lends credibility to recent non-representationalist and thoroughly probabilist interpretations of quantum mechanics. Two of them, that were already mentioned in the introduction, are Richard Healey’s “pragmatic” interpretation and Christopher Fuchs’ “QBism”.

According to Richard Healey [15], quantum theory is a new kind of nonrepresentational science ; it is fundamental but with no “beables” in it ; it is objective but its kind of objectivity consists in universal prescriptions to agents ; its symbols are purely predictive, not descriptive (therefore, famous features such as entanglement do not express a “real” entanglement of physical systems).

As for Christopher Fuchs [11], he considers that the symbols of quantum mechanics, such as state vectors, represent nothing more than a mathematical instrument used by agents in order to make optimal bets about the outcomes of future experiments. In Fuchs’ own terms, the quantum symbolism is just a “user’s manual” for each individual agent. If one wants to “explain” a phenomenon by quantum mechanics, the very meaning of the word explanation has to be changed: quantum mechanics explains why, in order to be coherent, an agent should assign probability p, not why a certain result has been obtained. Accordingly, standard paradoxes are dissolved by removing entirely the ontological import of symbols. A crucial example is the Einstein Podolsky Rosen paradox, that is dissolved by considering that predicting a future outcome with probability 1 does not imply the existence of Einstein’s “element of reality”, but only expresses the supreme confidence of agents.

In the wake of these deflationary interpretations of quantum mechanics, even the elementary presuppositions that measurement results are about “physical systems” has been challenged. The analysis of sequences of measurement outcomes, in the framework of the so-called “device-independent approaches” [13], has shown that their structure is generally incompatible with the concept of “permanent physical systems bearing properties”. This puts an end to the implicit but pervasive idea that quantum mechanics describes the exceptional features of certain microphysical systems ; it rather reveals the collapse of standard ontological patterns at the micro-scale, and the emergence of a context-dependent kind of knowledge [24]. This is enough to give full legitimacy to a transposition of quantum theory to the human sciences. For, even though there can be no common ontological domain between microphysics and the human sciences, there is a common epistemological approach that determines the structure and meaning of both disciplines.

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quantum machine learning

Why Should We Use Quantum Theory?

21

References

1.Apel, K.O.: L’a priori du corps dans le probl`eme de la connaissance. Cerf (2005)

2.Bitbol, M.: M´ecanique quantique, une introduction philosophique. Flammarion (1996)

3.Bitbol, M.: Some steps towards a transcendental deduction of quantum mechanics. Philosophia Nat. 35, 253–280 (1998)

4.Bitbol, M.: De l’int´erieur du monde: pour une philosophie et une science des relations. Flammarion (2011)

5.Bohr, N.: The Unity of Human Knowledge, Philosophical Writings of Niels Bohr, vol. 3. Ox Bow Press, Woodbridge (1960)

6.Cavaill`es, J.: Du collectif au pari, `apropos de quelques th´eories r´ecentes sur les probabilit´es. Revue de M´etaphysique et de Morale 47(2), 139–163 (1940)

7.Destouches, J.L.: Pr´evisions, calcul et r´ealit´es. Les Grands probl`emes des sciences (1965)

8.Destouches, J.L.: La M´ecanique Ondulatoire. Les Etudes Philosophiques 4(3), 473– 474 (1949)

9.Destouches-F´evrier, P.: La structure des th´eories physiques. Presses Universitaires de France (1951)

10.Eddington, A.S.: Space, Time and Gravitation. Cambridge University Press, Cambridge (1920)

11.Fuchs, C.A.: QBism, the perimeter of quantum Bayesianism. arXiv preprint arXiv:1003.5209 (2010)

12.Goldstein, K.: La structure de l’organisme. Gallimard, Paris (1951)

13.Grinbaum, A.: How device-independent approaches change the meaning of physical theory. Stud. Hist. Philos. Sci. Part B: Stud. Hist. Philos. Mod. Phys. 58, 22–30 (2017)

14.Habermas, J.: Logique des sciences sociales et autres essais. Presses Universitaires de France, Paris (1987)

15.Healey, R.: The Quantum Revolution in Philosophy. Oxford University Press, Oxford (2017)

16.Heelan, P.A.: Complementarity, context dependence, and quantum logic. Found. Phys. 1(2), 95–110 (1970). https://doi.org/10.1007/BF00708721

17.Heisenberg, W.: Philosophie, Manuscrit de 1942. Edition du Seuil (1998)

18.Hø ding, H.: Filosofiske Problemer. Univ. Bogtr. (1902)

19.Hø ding, H.: Relation som Kategori. Kluwer, Dordrecht (1921). Quoted by J. Faye, Niels Bohr, his Heritage and Legacy

20.Hjelmslev, L.: Prol´egom`enes `aune th´eorie du langage. Minuit (1971)

21.Meyer-Abich, K.: Bohr’s Complementarity and Goldstein’s holism in reflective pragmatism. Mind Matter 2, 91–103 (2004)

22.Petersen, A.: The philosophy of Niels Bohr. Bull. At. Sci. 19(7), 8–14 (1963). https://doi.org/10.1080/00963402.1963.11454520

23.Piaget, J.: Logique et connaissance scientifique. Gallimard-Pl´eiade, Paris (1967)

24.Rasmussen, M.: Le probl`eme de l’observation en linguistique. Une comparaison entre Louis Hjelmslev et Niels Bohr. Louis Hjelmslev et la s´emiotique contemporaine 24, 112 (1993)

25.Ryle, G.: The Concept of Mind. The University of Chicago Press, Chicago (1949)

26.Searle, J.: The Construction of Social Reality. Allen Lane, Bristol (1995)

27.Wang, H., Sun, Y.: On quantum models of the human mind. Top. Cogn. Sci. 6(1), 98–103 (2014)

28.Watanabe, S.: Algebra of observation. Prog. Theor. Phys. Suppl. 37, 350–367 (1966)

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quantum machine learning

Quantum Cognition

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quantum machine learning

The Power of Distraction: An

Experimental Test of Quantum

Persuasion

Ariane Lambert-Mogiliansky1(B), Adrian Calmettes2, and Herv´ Gonay3

1 Paris School of Economics, 48 boulevard Jourdan, 75014 Paris, France alambert@pse.ens.fr

2 Department of Political Science, The Ohio State University, 2140 Derby Hall,

154 N Oval Mall, Columbus, OH 43210, USA calmettes.1@osu.edu

3 GetQuanty, 54 rue Greneta, 75002 Paris, France herve.gonay@getquanty.com

Abstract. Quantum-like decision theory is by now a well-developed field. We here test the predictions of an application of this approach to persuasion as developed by Danilov and Lambert-Mogiliansky in [6]. One remarkable result entails that in contrast to Bayesian predictions, distraction rather than relevant information has a powerful potential to influence decision-making. We conducted an experiment in the context of donations to NGOs active in the protection of endangered species.

We first tested the quantum incompatibility of two perspectives ‘trust’ and ‘urgency’ in a separate experiment. We next recruited 1371 respondents and divided them into three groups: a control group, a first treatment group and the main treatment group. Our main result is that ‘distracting’ information significantly a ected decision-making: it induced a switch in respondents’ choice as to which project to support compared with the control group. The first treatment group which was provided with compatible information exhibited no di erence compared with the control group. Population variables play no role suggesting that quantum-like indeterminacy may indeed be a basic regularity of the mind. We thus find support for the thesis that the manipulability of people’s decision-making is linked to the quantum indeterminacy of their subjective representations (mental pictures) of the choice alternatives.

Keywords: Persuasion · Distraction · Information processing · Belief updating · Quantum cognition

1 Introduction

The theory of persuasion was initiated by Kamenica and Gentzkow [12] and further developed in a variety of directions. The subject matter of the theory of persuasion is the use of an information structure (or measurement) that generates new information in order to modify a person’s state of beliefs with the intent

c Springer Nature Switzerland AG 2019

B. Coecke and A. Lambert-Mogiliansky (Eds.): QI 2018, LNCS 11690, pp. 25–38, 2019. https://doi.org/10.1007/978-3-030-35895-2_2

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quantum machine learning

26 A. Lambert-Mogiliansky et al.

of making her act in a specific way. The question of interest is how much can a person, call him Sender, influence another one, call her Receiver, by selecting a suitable measurement and revealing its outcome. An example is in lobbying. A pharmaceutical company commissions to a scientific laboratory a specific study of a drug impact, the result of which is delivered to the politician. The question of interest from a persuasion point of view is what kind of study best serves the company’s interest.

Receiver’s decision to act depends on her beliefs about the world. In [12] and related works, the beliefs are given as a probability distribution over a set of states of the world. A central assumption is that uncertainty is formulated in the standard classical framework. As a consequence the updating of Receiver’s beliefs follows Bayes’ rule.

However as amply documented the functioning of the mind is more complex and people often do not follow Bayes rule. Cognitive sciences propose alternatives to Bayesianism. One avenue of research within cognitive sciences appeals to the formalism of quantum mechanics (QM). A main reason is that QM has properties that reminds of the paradoxical phenomena exhibited in human cognition. As argued by Danilov and Lambert-Mogiliansky in [6], there also exists deeper reasons for turning to quantum mechanics when studying human behavior. Moreover cognition has been successful in explaining a wide variety of behavioral phenomena such as disjunction e ect, cognitive dissonance, order e ects or preference reversal (see [3, 10]). Finally, there exists by now a fully developed decision theory relying on the principle of quantum cognition (see [7]). Clearly, the mind is likely to be even more complex than a quantum system but our view is that the quantum cognitive approach already delivers interesting new insights in particular with respect to persuasion.

In quantum cognition, the system of interest is the decision-maker’s mental representation of the world. It is represented by a cognitive state. This repre-

sentation of the world is modelled as a quantum-like system so the decision relevant uncertainty is of non-classical (quantum) nature. This modelling approach allows capturing widespread cognitive di culties that people exhibit when constructing a mental representation of a ‘complex’ alternative (cf, [4]). The key quantum property that we use is that some characteristics (cf. properties) of a complex mental object may be “Bohr complementary” that is incompatible in the decision-maker’s mind: she cannot combine in a stable way pieces of information from the two perspectives. A central implication is that measurements (new information) modifies the cognitive state in a non-Bayesian well-defined manner.

As in the classical context our rational Receiver uses new information to update her beliefs so that choices based on updated preferences are consistent with ex-ante preferences defined for the condition (event) that triggered updating. In [7], we learned that a dynamically consistent rational quantum-like decision-maker updates her beliefs according to the von Neumann-L¨uders postulate. In two recent papers, important theoretical results were established. First, as shown in [5], in the absence of any constraints on measurements, full persuasion applies: Sender can always persuade Receiver to believe anything that favors