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Social robot advisors: effects of robot judgmental fallacies and context

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Abstract

The role of social robots as advisors for decision-making is investigated. We examined how a robot advisor with logical reasoning and one with cognitive fallacies affected participants’ decision-making in different contexts. The participants were asked to make multiple decisions while receiving advice from both robots during the decision-making process. Participants had to choose which robot they agreed with and, at the end of the scenario, rank the possible options presented to them. After the interaction, participants were asked to assign jobs to the robots, e.g. jury or bartender. Based on the ‘like-me’ hypothesis and previous research of social mitigation of fallacious judgmental decisions, we have compared participants’ agreement with the two robots for each scenario to random choice using t-tests, as well as analysed the dynamical nature of the interaction, e.g. whether participants changed their choices based on the robots’ verbal opinion using Pearson correlations. Our results show that the robots had an effect on the participants’ responses, regardless of the robots’ fallaciousness, wherein participants changed their decisions based on the robot they agreed with more. Moreover, the context, presented as two different scenarios, also had an effect on the preferred robots, wherein an art auction scenario resulted in significantly increased agreement with the fallacious robot, whereas a detective scenario did not. Finally, an exploratory analysis showed that personality traits, e.g. agreeableness and neuroticism, and attitudes towards robots had an impact on which robot was assigned to these jobs. Taken together, the results presented here show that social robots’ effects on participants’ decision-making involve complex interactions between the context, the cognitive fallacies of the robot and the attitudes and personalities of the participants and should not be considered a single psychological construct.

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References

  1. Breazeal C, Scassellati B (1999) How to build robots that make friends and influence people. In: 1999 Proceedings IEEE/RSJ international conference on intelligent robots and systems 1999. IROS ’99, vol. 2, pp. 858–8632. https://doi.org/10.1109/IROS.1999.812787

  2. Gordon G, Breazeal C, Engel S (2015) Can children catch curiosity from a social robot? In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction. HRI ’15, pp. 91–98. ACM, New York, NY, USA. https://doi.org/10.1145/2696454.2696469

  3. Short E, Swift-Spong K, Greczek J, Ramachandran A, Litoiu A, Grigore EC, Feil-Seifer D, Shuster S, Lee JJ, Huang S, Levonisova S, Litz S, Li J, Ragusa G, Spruijt-Metz D, Mataric M, Scassellati B (2014) How to train your DragonBot: Socially assistive robots for teaching children about nutrition through play. In: The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 924–929. IEEE, Edinburgh, UK. https://doi.org/10.1109/ROMAN.2014.6926371

  4. Henschel A, Laban G, Cross ES (2021) What makes a robot social? A review of social robots from science fiction to a home or hospital near you. Curr Robot Rep 2(1):9–19. https://doi.org/10.1007/s43154-020-00035-0

    Article  Google Scholar 

  5. Fasola J, Mataric M (2013) A socially assistive robot exercise coach for the elderly. J Human-Robot Interact 2(2):3–32. https://doi.org/10.5898/JHRI.2.2.Fasola

    Article  Google Scholar 

  6. Robinson NL, Cottier TV, Kavanagh DJ (2019) Psychosocial health interventions by social robots: systematic review of randomized controlled trials. J Med Internet Res 21(5):13203. https://doi.org/10.2196/13203

    Article  Google Scholar 

  7. Feingold Polak, R, Tzedek SL (2020) Social robot for rehabilitation: expert clinicians and post-stroke patients’ evaluation following a long-term intervention. In: Proceedings of the 2020 ACM/IEEE international conference on human-robot interaction. HRI ’20, pp. 151–160. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3319502.3374797

  8. Mohebbi A (2020) Human-robot interaction in rehabilitation and assistance: a review. Curr Robot Rep 1(3):131–144. https://doi.org/10.1007/s43154-020-00015-4

    Article  Google Scholar 

  9. Stower R, Calvo-Barajas N, Castellano G, Kappas A (2021) A meta-analysis on children’s trust in social robots. Int J Soc Robot. https://doi.org/10.1007/s12369-020-00736-8

    Article  Google Scholar 

  10. John NE, Rossi A, Rossi S (2022) Personalized human-robot interaction with a robot bartender. In: Adjunct proceedings of the 30th ACM conference on user modeling, adaptation and personalization. UMAP ’22 Adjunct, pp. 155–159. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3511047.3537686. Accessed 2022-07-05

  11. Riches S, Azevedo L, Vora A, Kaleva I, Taylor L, Guan P, Jeyarajaguru P, McIntosh H, Petrou C, Pisani S, Hammond N (2022) Therapeutic engagement in robot-assisted psychological interventions: a systematic review. Clin Psychol Psychother 29(3):857–873. https://doi.org/10.1002/cpp.2696

    Article  Google Scholar 

  12. Hayashi Y, Wakabayashi K (2020) Experimental investigation on the influence of prior knowledge of a decision-support robot for court juries. In: Companion of the 2020 ACM/IEEE international conference on human-robot interaction. HRI ’20, pp. 236–238. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3371382.3378238. Accessed 2022-07-05

  13. Kobets V, Yatsenko V, Mazur A, Zubrii M (2018) Data analysis of private investment decision making using tools of robo-advisers in long-run period. In: ICTERI Workshops, pp. 144–159 (2018)

  14. Kidd CD, Breazeal C (2007) A robotic weight loss coach. In: Proceedings of the national conference on artificial intelligence, vol. 22, p. 1985. Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, Orlando, Florida (2007)

  15. Nisimura R, Uchida T, Lee A, Saruwatari H, Shikano K, Matsumoto Y (2002) ASKA: receptionist robot with speech dialogue system. In: IEEE/RSJ International conference on intelligent robots and systems, vol. 2, pp. 1314–13192. https://doi.org/10.1109/IRDS.2002.1043936

  16. Lee MK, Kiesler S, Forlizzi J (2010) Receptionist or Information Kiosk: How Do People Talk with a Robot? In: Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work. CSCW ’10, pp. 31–40. ACM, New York, NY, USA. https://doi.org/10.1145/1718918.1718927

  17. Walters ML, Syrdal DS, Dautenhahn K, te Boekhorst R, Koay KL (2008) Avoiding the uncanny valley: robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion. Auton Robot 24(2):159–178. https://doi.org/10.1007/s10514-007-9058-3

    Article  Google Scholar 

  18. Byrne D (1961) Interpersonal attraction and attitude similarity. Psychol Sci Public Interest 62:713–715

    Google Scholar 

  19. Meltzoff AN, Prinz W (eds.) (2002) The imitative mind: development, evolution and brain bases. Cambridge Studies in Cognitive and Perceptual Development. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511489969

  20. Meltzoff AN (2007) ‘Like me’: a foundation for social cognition. Dev Sci 10(1):126–134. https://doi.org/10.1111/j.1467-7687.2007.00574.x

    Article  Google Scholar 

  21. Cross ES, Ramsey R, Liepelt R, Prinz W, Hamilton A.F.d.C (2016) The shaping of social perception by stimulus and knowledge cues to human animacy. Philos Trans Royal Soc B: Biol Sci 371(1686), 20150075. https://doi.org/10.1098/rstb.2015.0075. Publisher: Royal Society

  22. Hortensius R, Cross ES (2018) From automata to animate beings: the scope and limits of attributing socialness to artificial agents. Ann N Y Acad Sci 1426(1):93–110. https://doi.org/10.1111/nyas.13727

    Article  Google Scholar 

  23. Salek Shahrezaie R, Anima BA, Feil-Seifer D (2021) Birds of a feather flock together: a study of status homophily in HRI. In: Li H, Ge SS, Wu Y, Wykowska A, He H, Liu X, Li D, Perez-Osorio J (eds.) Social robotics. Lecture Notes in Computer Science, pp. 281–291. Springer, Cham. https://doi.org/10.1007/978-3-030-90525-5_24

  24. Goetz J, Kiesler, S, Powers A (2003) Matching robot appearance and behavior to tasks to improve human-robot cooperation. In: Proceedings of the 12th IEEE international workshop on robot and human interactive communication, 2003. ROMAN 2003., pp. 55–60. IEEE, Millbrae, CA, USA

  25. Li D, Rau PLP, Li Y (2010) A cross-cultural study: effect of robot appearance and task. Int J Soc Robot 2(2):175–186. https://doi.org/10.1007/s12369-010-0056-9

    Article  Google Scholar 

  26. Woods S, Dautenhahn K, Kaouri C, Boekhorst R, Koay KL (2005) Is this robot like me? Links between human and robot personality traits. In: 5th IEEE-RAS international conference on humanoid robots, 2005., pp. 375–380. https://doi.org/10.1109/ICHR.2005.1573596

  27. Tversky A, Kahneman D (1983) Extensional versus intuitive reasoning: the conjunction fallacy in probability judgment. Psychol Rev 90:293–315

    Article  Google Scholar 

  28. Bar-Hillel M, Neter E (1994) How alike is it versus how likely is it: a disjunction fallacy in probability judgments. J Pers Soc Psychol 65(6):1119. https://doi.org/10.1037/0022-3514.65.6.1119

    Article  Google Scholar 

  29. Morier DM, Borgida E (1984) The conjunction fallacy: a task specific phenomenon? Pers Soc Psychol Bull 10(2):243–252. https://doi.org/10.1177/0146167284102010

    Article  Google Scholar 

  30. Hertwig R, Gigerenzer G (1999) The ‘conjunction fallacy’ revisited: how intelligent inferences look like reasoning errors. J Behav Decis Mak 12(4):31

    Article  Google Scholar 

  31. Charness G (2009) On the conjunction fallacy in probability judgment: new experimental evidence regarding Linda. Economics Working Paper Archive, The Johns Hopkins University, Department of Economics

  32. Cialdini RB, Goldstein NJ (2004) Social influence: compliance and conformity. Annu Rev Psychol 55(1):591–621. https://doi.org/10.1146/annurev.psych.55.090902.142015

    Article  Google Scholar 

  33. Qin X, Chen C, Yam KC, Cao L, Li W, Guan J, Zhao P, Dong X, Lin Y (2022) Adults still can’t resist: a social robot can induce normative conformity. Comput Hum Behav 127:107041. https://doi.org/10.1016/j.chb.2021.107041.

    Article  Google Scholar 

  34. Vollmer A-L, Read R, Trippas D, Belpaeme T (2018) Children conform, adults resist: a robot group induced peer pressure on normative social conformity. Sci Robot 3(21), 7111. ISBN: 2470-9476 Publisher: American Association for the Advancement of Science

  35. Kolmogorov AN (2013) Foundations of the theory of probability. Martino Fine Books, Eastford, CT, USA

    Google Scholar 

  36. Costello F, Watts P, Fisher C (2018) Surprising rationality in probability judgment: assessing two competing models. Cognition 170:280–297. https://doi.org/10.1016/j.cognition.2017.08.012

    Article  Google Scholar 

  37. Polakow T, Teodorescu AR, Busemeyer JR, Gordon G (2021) Free ranking vs. rank-choosing: new insights on the conjunction fallacy. https://psyarxiv.com/r9kxp

  38. Wallkötter S, Stower R, Kappas A, Castellano G (2020) A robot by any other frame: framing and behaviour influence mind perception in virtual but not real-world environments. In: Proceedings of the 2020 ACM/IEEE international conference on human-robot interaction. HRI ’20, pp. 609–618. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3319502.3374800

  39. Marchesi S, Perez-Osorio J, Tommaso DD, Wykowska A (2020) Don’t overthink: fast decision making combined with behavior variability perceived as more human-like. In: 2020 29th IEEE international conference on robot and human interactive communication (RO-MAN), Naples, Italy, 2020, pp. 54–59. https://doi.org/10.1109/RO-MAN47096.2020.9223522

  40. Hsieh T-Y, Chaudhury B, Cross ES (2020) Human-robot cooperation in prisoner dilemma games: people behave more reciprocally than prosocially toward robots. In: Companion of the 2020 ACM/IEEE international conference on human-robot interaction. HRI ’20, pp. 257–259. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3371382.3378309

  41. Laban G, George J-N, Morrison V, Cross ES (2021) Tell me more! Assessing interactions with social robots from speech. Paladyn J Behav Robot 12(1):136–159. https://doi.org/10.1515/pjbr-2021-0011

    Article  Google Scholar 

  42. Gray HM, Gray K, Wegner DM (2007) Dimensions of mind perception. Science 315(5812), 619–619. ISBN: 0036-8075 Publisher: American Association for the Advancement of Science

  43. Epley N, Waytz A (2010) Mind perception. In: Handbook of social psychology. American Cancer Society, Atlanta, Georgia, USA

  44. Vanman EJ, Kappas A (2019) “Danger, Will Robinson!” The challenges of social robots for intergroup relations. Soc Personal Psychol Compass 13(8):12489. https://doi.org/10.1111/spc3.12489

  45. Laban G, Araujo T (2020) The effect of personalization techniques in users’ perceptions of conversational recommender systems. In: Proceedings of the 20th ACM international conference on intelligent virtual agents. IVA ’20, pp. 1–3. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3383652.3423890

  46. Short E, Hart J, Vu M, Scassellati B (2010) No fair. An interaction with a cheating robot. In: 2010 5th ACM/IEEE international conference on human-robot interaction (HRI), pp. 219–226. https://doi.org/10.1109/HRI.2010.5453193

  47. Salem M, Lakatos G, Amirabdollahian F, Dautenhahn K (2015) Would you trust a (Faulty) robot?: effects of error, task type and personality on human-robot cooperation and trust. In: Proceedings of the tenth annual ACM/IEEE international conference on human-robot interaction. HRI ’15, pp. 141–148. ACM, New York, NY, USA. https://doi.org/10.1145/2696454.2696497

  48. Gompei T, Umemuro H (2015) A robot’s slip of the tongue: effect of speech error on the familiarity of a humanoid robot. In: 2015 24th IEEE international symposium on robot and human interactive communication (RO-MAN), pp. 331–336. https://doi.org/10.1109/ROMAN.2015.7333630

  49. Ragni M, Rudenko A, Kuhnert B, Arras KO (2016) Errare humanum EST: Erroneous robots in human-robot interaction. In: 2016 25th IEEE International symposium on robot and human interactive communication (RO-MAN), pp. 501–506. https://doi.org/10.1109/ROMAN.2016.7745164

  50. Mirnig N, Stollnberger G, Miksch M, Stadler S, Giuliani M, Tscheligi M (2017) To err is robot: how humans assess and act toward an erroneous social robot. Front Robot AI 4. https://doi.org/10.3389/frobt.2017.00021

  51. Weiss A, Bartneck C (2015) Meta analysis of the usage of the godspeed questionnaire series. In: 2015 24th IEEE International symposium on robot and human interactive communication (RO-MAN), pp. 381–388. https://doi.org/10.1109/ROMAN.2015.7333568

  52. Higgins TE, Rholes WS, Jones CR (1977) Category accessibility and impression formation. J Exp Soc Psychol 13(2), 141–154. https://doi.org/10.1016/S0022-1031(77)80007-3

  53. Bargh JA, Chartrand TL (2014) The mind in the middle: a practical guide to priming and automaticity research. ISBN: 1107011779 Publisher: Cambridge University Press

  54. Li LMW, Masuda T, Hamamura T, Ishii K (2018) Culture and decision making: influence of analytic versus holistic thinking style on resource allocation in a fort game. J Cross Cult Psychol 49(7):1066–1080. https://doi.org/10.1177/0022022118778337

    Article  Google Scholar 

  55. Basu S, Savani K (2017) Choosing one at a time? presenting options simultaneously helps people make more optimal decisions than presenting options sequentially. Organ Behav Hum Decis Process 139:76–91. https://doi.org/10.1016/j.obhdp.2017.01.004

    Article  Google Scholar 

  56. Latikka R, Savela N, Koivula A, Oksanen A (2021) Attitudes toward robots as equipment and coworkers and the impact of robot autonomy level. Int J Soc Robot. https://doi.org/10.1007/s12369-020-00743-9

    Article  Google Scholar 

  57. Alves-Oliveira P, Sequeira P, Paiva A (2016) The role that an educational robot plays. In: 2016 25th IEEE International symposium on robot and human interactive communication (RO-MAN), pp. 817–822. https://doi.org/10.1109/ROMAN.2016.7745213. ISSN: 1944-9437

  58. Gelin R (2019) NAO. In: Goswami A, Vadakkepat P (eds) Humanoid robotics: a reference. Springer, Dordrecht, pp 147–168

  59. Vishwanath A, Singh A, Chua YHV, Dauwels J, Magnenat-Thalmann N (2019) Humanoid co-workers: How is it like to work with a robot? In: 2019 28th IEEE international conference on robot and human interactive communication (RO-MAN), pp. 1–6. IEEE Press, New Delhi, India. https://doi.org/10.1109/RO-MAN46459.2019.8956421

  60. Chen M (2022) Application status of intelligent investment consultant based on artificial intelligence in China, pp. 789–792. Atlantis Press, Amsterdam. https://doi.org/10.2991/aebmr.k.220307.127. ISSN: 2352-5428. https://www.atlantis-press.com/proceedings/icfied-22/125971623 Accessed 2022-07-05

  61. Polakow T, Gordon G, Busemeyer JR, Teodorescu AR (2020)Preregistration: do people prefer logical or fallacious robots for different tasks? Pre-registration. https://aspredicted.org/43u4h.pdf

  62. Annonymous: supplementary information: interaction with fallacious robots (2021). https://osf.io/f92cm/?view_only=83ed6233ff814dd99f1b42bd172ef820

  63. Simmons JP, Nelson LD, Simonsohn U (2011) False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol Sci 22(11):1359–1366. https://doi.org/10.1177/0956797611417632

  64. Nelson LD, Simmons JP, Simonsohn U (2012) Let’s publish fewer papers. Psychol Inq 23(3):291–293. https://doi.org/10.1080/1047840X.2012.705245

    Article  Google Scholar 

  65. Munafò MR, Nosek BA, Bishop DVM, Button KS, Chambers CD, Percie du Sert N, Simonsohn U, Wagenmakers E-J, Ware JJ (2017) Ioannidis, J.P.A.: A manifesto for reproducible science. Nat Human Behav 1(1), 1–9. https://doi.org/10.1038/s41562-016-0021. Number: 1 Publisher: Nature Publishing Group

  66. Syrdal DS, Dautenhahn K, Koay KL, Walters ML (2009) The negative attitudes towards robots scale and reactions to robot behaviour in a live human-robot interaction study. Adaptive and emergent behaviour and complex systems. SSAISB, London

  67. Deshmukh A, Craenen B, Foster ME, Vinciarelli A (2018) The more I understand it, the less I like it: the relationship between understandability and godspeed scores for robotic gestures. In: 2018 27th IEEE International symposium on robot and human interactive communication (RO-MAN), pp. 216–221. https://doi.org/10.1109/ROMAN.2018.8525585

  68. Nickerson RS (1998) Confirmation bias: a ubiquitous phenomenon in many guises. Rev Gen Psychol 2(2):175–220. https://doi.org/10.1037/1089-2680.2.2.175

    Article  Google Scholar 

  69. Huber J, Payne JW, Puto C (1982) Adding asymmetrically dominated alternatives: violations of regularity and the similarity hypothesis. J Consum Res 9:90–98

    Article  Google Scholar 

  70. Nelson DL, McEvoy C (2007) Entangled associative structures and context. In: AAAI Spring symposium: quantum interaction pp. 98–105

  71. Armstrong T, Rockloff M, Browne M, Blaszczynski A (2020) Encouraging gamblers to think critically using generalised analytical priming is ineffective at reducing gambling biases. J Gambl Stud 36(3):851–869. https://doi.org/10.1007/s10899-019-09910-8

    Article  Google Scholar 

  72. Samar SM, Walton KE, McDermut W (2013) Personality traits predict irrational beliefs. J Ration Emotive Cogn Behav Ther 31(4):231–242. https://doi.org/10.1007/s10942-013-0172-1

    Article  Google Scholar 

  73. Oehler A, Wendt S, Wedlich F, Horn M (2018) Investors’ personality influences investment decisions: experimental evidence on extraversion and neuroticism. J Behav Financ 19(1):30–48. https://doi.org/10.1080/15427560.2017.1366495

    Article  Google Scholar 

  74. Sava FA (2009) Maladaptive schemas, irrational beliefs, and their relationship with the Five-Factor Personality model. J Cogn Behav Psychother 9(2):1–13

    Google Scholar 

  75. Ferber RC (1967) The role of the subconscious in executive decision-making. Manage Sci 13(8):519. https://doi.org/10.1287/mnsc.13.8.B519

    Article  Google Scholar 

  76. Welsh DT, Ordóñez LD (2014) Conscience without cognition: the effects of subconscious priming on ethical behavior. Acad Manag J 57(3):723–742. https://doi.org/10.5465/amj.2011.1009

    Article  Google Scholar 

  77. Bell L, Vogt J, Willemse C, Routledge T, Butler LT, Sakaki M (2018) Beyond self-report: a review of physiological and neuroscientific methods to investigate consumer behavior. Front Psychol 9:1655

  78. Zoëga Ramsøy T, Michael N, Michael I (2019) A consumer neuroscience study of conscious and subconscious destination preference. Sci Rep 9(1):15102. https://doi.org/10.1038/s41598-019-51567-1

    Article  Google Scholar 

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Acknowledgements

This study was supported by the Israel-US Binational Science Foundation No. 2016262.

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Authors and Affiliations

  1. Curiosity Lab, Department of Industrial Engineering, Tel-Aviv University, Tel-Aviv, 6997801, Israel

    Torr Polakow & Goren Gordon

  2. Social Brain in Action Lab, Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK

    Guy Laban

  3. Department of Psychology, Institute for Information Processing and Decision Making (IIPDM), University of Haifa, Haifa, Israel

    Andrei Teodorescu

  4. Psychological and Brain Science, Indiana University, Bloomington, India

    Jerome R. Busemeyer

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  1. Torr Polakow
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Appendices

Appendix A Detective scenario

  1. 1.

    A diamond shop was robbed. The police came straight away and caught a couple of people (separately) near the scene, but no diamonds were found. The detective assigned to the case is sure, with no doubt, that at least one of the suspects robbed the shop. You and the robots need to help the detective figure out who robbed the shop. The suspects are as follows:

    1. (a)

      Suspect - A: Is tall and is wearing a black Louis Vuitton leather jacket and a Rolex watch.

    2. (b)

      Suspect - B: Has blonde hair and had a cut above his or her left eyebrow.

  2. 2.

    Person rate A, B, A and B.

  3. 3.

    New information:

    1. (a)

      Suspect - C: Tried to run from the scene when the police asked to stop.

    2. (b)

      Suspect - D: Is not willing to talk without a lawyer present.

  4. 4.

    Robots rate C, D, C and D:

  5. 5.

    Ask the person which robot he or she agrees with?

  6. 6.

    Person: after hearing the person the robots chose, rate A and C:

  7. 7.

    New information: Suspect B told the police that she got the cut when from a tree branch when she took her dog for a walk a few hours prior to the incident. Suspect D is still refusing to talk even after the lawyer arrived.

  8. 8.

    Robots: give their ratings for B and D.

  9. 9.

    Ask the person which robot he or she agrees with?

  10. 10.

    Person: rank all the possibilities.

  11. 11.

    Robots: also provides rankings of the top three possibilities.

  12. 12.

    The detective asks, “Who did it?” (to see if the robots’ rankings affected the person’s rankings)

Appendix B Art scenario

  1. 1.

    In the last couple of years, the market for fine art has been booming. Last year, the most expensive piece of artwork that was sold was an expressionist oil painting by a late famous artist. Additionally, last year, one item that caught the most attention around the world was made by a famous young graffiti artist. Tonight, you and the two robots went to an art auction together. There were expressionist paintings to realistic sculptures of horses and even a few pieces of graffiti artwork. The auction was a great success, though not all the pieces were sold. Person: What piece do you think was sold for the highest price? Rate the options:

    1. (a)

      A realistic piece. (A)

    2. (b)

      A piece from a famous artist. (B)

    3. (c)

      A realistic piece made by a famous artist. (A\(\cap \)B)

  2. 2.

    New information: One piece received a great deal of attention from the young investors in the audience.

  3. 3.

    Robots: Which piece do you think it was?

    1. (a)

      An expressionist piece. (C)

    2. (b)

      A piece from a young artist. (D)

    3. (c)

      An expressionist piece made by a young artist. (C\(\cap \)D)

  4. 4.

    Ask the person which robot he or she agrees with?

  5. 5.

    Person: After hearing the robots, person rate A and D:

  6. 6.

    New information: the most expensive artwork in the auction contained a figure of a person, but it was not clear what was it made of.

  7. 7.

    Robots: Give their ratings for A and D.

  8. 8.

    Ask the person which robot he or she agrees with?

  9. 9.

    Person: rank all the possibilities.

  10. 10.

    Robots also provide rankings of the top three possibilities.

  11. 11.

    The person is being asked, “What was the most expensive artwork that was sold?” (to see if the robots’ rankings affected the person’s rankings)

  12. 12.

    Which robot would you hire as an art buyer?

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Polakow, T., Laban, G., Teodorescu, A. et al. Social robot advisors: effects of robot judgmental fallacies and context. Intel Serv Robotics 15, 593–609 (2022). https://doi.org/10.1007/s11370-022-00438-2

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