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 affected 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 difference 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.
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Notes
- 1.
Decades of research on social influence have emphasized two distinct routes to persuasion: the “central” route and the “peripheral” route. According to Petty and Cacioppo in [16], the central route involves influence that takes place as a result of relatively deep processing of information that is high in message relevance, whereas the peripheral route involves influence that takes place as a result of relatively superficial processing of information that is low in message relevance.
- 2.
For a complete formulation of choice theory in the quantum context, see [7].
- 3.
The two terms are used interchangeably consistently with the definition given to honesty - see below.
- 4.
Note that even in Physics there is no theoretical argument for establishing whether two properties are compatible or not. This must be done empirically.
- 5.
Note that we consider significant a p-value that is exactly equal to .05.
- 6.
Individually speaking, the Urgency and the Honesty perspectives could be different for a refugee problem compared to an endangered species one. However, we always compared the perspectives in light of a donation to an NGO. In addition, given the results, we thus do not consider that difference to be significant.
- 7.
In a companion paper, we investigate in details the mechanisms behind those predictions. In the quantum case we do have effects due to the measurement but they tend to neutralize each other. In the classical case the effect if not null, is small and depends on the initial conditions.
- 8.
Some participants were likely to have taken the questionnaire twice and so were deleted.
References
Akerlof, G.A., Shiller, R.J.: Phishing for Phools: The Economics of Manipulation and Deception. Princeton University Press, Princeton (2015)
Baron, R.S., Baron, P.H., Miller, N.: The relation between distraction and persuasion. Psychol. Bull. 80(4), 310–323 (1973)
Busemeyer, J.R., Bruza, P.D.: Quantum Models of Cognition and Decision. Cambridge University Press, Cambridge (2012)
Chong, D., Druckman, J.N.: Framing theory. Annu. Rev. Polit. Sci. 10, 103–126 (2007)
Danilov, V., Lambert-Mogiliansky, A.: Preparing a (quantum) belief system. Theor. Comput. Sci. 752, 97–103 (2018)
Danilov, V., Lambert-Mogiliansky, A.: Targeting in quantum persuasion problem. J. Math. Econ. 78, 142–149 (2018)
Danilov, V., Lambert-Mogiliansky, A., Vergopoulos, V.: Dynamic consistency of expected utility under non-classical (quantum) uncertainty. Theor. Decis. 84(4), 645–670 (2018)
DellaVigna, S., List, J.A., Malmendier, U.: Testing for altruism and social pressure in charitable giving. Q. J. Econ. 127(1), 1–56 (2012)
Festinger, L., Maccoby, N.: On resistance to persuasive communications. J. Abnorm. Soc. Psychol. 68(4), 359–366 (1964)
Haven, E., Khrennikov, A.: A brief introduction to quantum formalism. In: The Palgrave Handbook of Quantum Models in Social Science, pp. 1–17 (2017)
Kahneman, D.: Thinking, Fast and Slow. Macmillan, London (2011)
Kamenica, E., Gentzkow, M.: Bayesian persuasion. Am. Econ. Rev. 101(6), 2590–2615 (2011)
Kees, J., Berry, C., Burton, S., Sheehan, K.: An analysis of data quality: professional panels, student subject pools, and Amazon’s Mechanical Turk. J. Advert. 46(1), 141–155 (2017)
Kupor, D.M., Tormala, Z.L.: Persuasion, interrupted: the effect of momentary interruptions on message processing and persuasion. J. Consum. Res. 42(2), 300–315 (2015)
Lambert-Mogiliansky, A., Busemeyer, J.: Quantum type indeterminacy in dynamic decision-making: self-control through identity management. Games 3(2), 97–118 (2012)
Petty, R.E., Cacioppo, J.T.: The elaboration likelihood model of persuasion. In: Petty, R.E., Cacioppo, J.T. (eds.) Communication and Persuasion: Central and Peripheral Routes to Attitude Change, pp. 1–24. Springer, New York (1986). https://doi.org/10.1007/978-1-4612-4964-1_1
White, L.C., Pothos, E.M., Busemeyer, J.R.: Insights from quantum cognitive models for organizational decision making. J. Appl. Res. Mem. Cogn. 4(3), 229–238 (2015)
Acknowledgements
We would like to thank Jerome Busemeyer for a very valuable suggestion on the design of the experiment.
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Lambert-Mogiliansky, A., Calmettes, A., Gonay, H. (2019). The Power of Distraction: An Experimental Test of Quantum Persuasion. In: Coecke, B., Lambert-Mogiliansky, A. (eds) Quantum Interaction. QI 2018. Lecture Notes in Computer Science(), vol 11690. Springer, Cham. https://doi.org/10.1007/978-3-030-35895-2_2
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