Elnaz Nouri, David Traum Institute for Creative Technologies, USC

1. A Cross-cultural Study of Playing Simple Economic Games Online with Humans and Virtual Humans Elnaz Nouri, David TraumInstitute for Creative Technologies, USC HCII 2013 - 25th July Las Vegas

2. Abstract We ran simple online economic interactions between virtual human and people from two countries (India and US). We compare our results to: • Previously reported findings of similar interactions run in the laboratory. Virtual human

3. Questions we try to answer • Opponent’s effect: How similar or different do participants feel and act when playing a virtual human versus another person? • Culture’s effect: How different are players from the United States from Players in India? • Game’s effect: What impact does the type of game have on players’ decisions and values?

4. In this talk • Previous work: • Cultural differences in decision-making games and values • MARV model and MARV survey • Social aspects of human-agent interaction • On-line AMT Study and experiment details • US and Indian participants • Human vs. Virtual Humans • Dictator or Ultimatum game • Results • Cross-cultural differences in game play and personal values • Game effect • Opponent effect • Conclusion

5. Previous Work In-person games have been for understanding people's economic decision making behavior. (Camerer, 2003) • Ultimatum game(Guth, 1983), • Dictator game(Bolton, 1998), • Prisoner’s Dilemma (Rapoport, 1965) • …

6. An example: Ultimatum Game • Two players can split a certain amount of money (Güth, 1982). Two turn game: • Proposer: make an offer • Responder: accept or reject the offer • Expected Results: Accept any offer greater than zero Offer the minimum amount possible

7. Cultural Variations in Ultimatum Game Observations: Proposers offer about 40 – 50$ on average. Responders reject offers of 20$ or less. (Camerer, 2003) How can we explain these? Considerable variation of offers and acceptance rates across 4 cultures (Roth 1993; Camerer 2003) (Roth, 1993)

8. MARV Model (Nouri, 2011) • MARV = Multi-Attribute Relational Values • Goal: Internal computational model of decision making for agents that is sensitive to culture,and produces behavior consistent with observations of that culture • Approach: applying multi-attribute decision-making model to calculate utility of decisions • assigning appropriate weights to each of the following attributes: relative gain of the players agent’s own gain gain of another the total gain

9. Methods for setting up the weights VS08 Hofestede Survey Questions: have sufficient time for your personal or home life (IDV) Q2 have a boss (direct superior) you can respect (PDI) get recognition for good performance (MAS) have security of employment (IDV) have pleasant people to work with (MAS) do work that is interesting (IDV) be consulted by your boss in decisions involving your work (PDI) live in a desirable area (MAS) have a job respected by your family and friends (IDV) have chances for promotion (MAS) keeping time free for fun (IVR) moderation: having few desires (IVR) being generous to other people (MON) modesty: looking small, not big (MON) If there is something expensive you really want to buy but you do not have enough money, what do you do? (LTO) How often do you feel nervous or tense?(UAI) Are you a happy person? (IVR) Are you the same person at work (or at school if you’re a student) and at home? (LTO) … • Previous Methods for determining culture-specific weights on attributes: • Intuitions based on Hofstede’s dimensional model of culture (Nouri & Traum CMVC 2011) • Machine learning from human behavior data (Nouri et al CogSci 2012) New methodDirectly ask participants how much they care about the weights (Nouri and Traum GDN 2013) by assigning weights from -5 to 5. (-5 means they don’t care about that value whereas 5 shows they care significantly about the value.) Hofstededimensions of cultural values PDI:Power Distance (large vs. small), IDV: Individualism vs. Collectivism, MAS:Masculinity vs. Femininity, UAI:Uncertainty Avoidance (strong vs. weak), LTO: Long- vs. Short-Term Orientation, IVR:Indulgence vs. Restraint, and MON:Monumentalism vs. Self-Effacement. Example values reported by group A and B

10. MARV Survey Rating scale: from -5 (no importance at all) to 5 (significantly imporant)

11. Experiment Set Up • Played online version Ultimatum Game or Dictator Game over 100 points • The Ultimatum Game.as described. • The Dictator Game. played exactly like the standard Ultimatum Game, except that the responder is not given an opportunity to accept or reject the offer. • Paid based on their performance in the game: • $0.5 show up fee • Could earn another $0.05 for each additional 10 points that they accumulated in the game.

12. Snapshot of the SimCoachcharacterhttp://labs.simcoach.org/simcoach/?space=mini&character=3072

13. Experiment Procedure • Before Game: • Fill out the VS08 Hofstede Survey and demographic information questions • Receive instructions about the game (Dictator Game or Ultimatum Game) denoting they would be playing with another participant from their country. • The Game: • Play the proposer in Dictator Game or Ultimatum game • Fill out the MARV Decision-making values survey • (in the case of the ultimatum game) Receive their partner’s move and their final reward.

14. Study Participants • Indian and US participants recruited on Amazon Mechanical Turk.

15. Results

16. Results: Game Effect Offer Distribution Offers: The offers made in the two games are significantly different from one another. • Values: • are significantly different between the two games: • {Vother , Vcompete, Vequal , Vjoint, Vrawls, Vlower bound}

17. Results: Culture’s Effect US India Offers: Significant difference between the two cultures when playing ultimatum game with Virtual humans (p value<0.05). Values: Significance difference between the values reported by Indians and Americans (across all conditions): { Vself, Vcompete, Vchance} When playing with Virtual Humans { Vother in dictator game,Vlowerbound in ultimatum game}

18. Results: Opponent’s Effect • Offers: Playing against a virtual human or a human does not bring about significant difference in the offers made in the games. Indians played differently when playing ultimatum game with a virtual human as opposed to a human (p value<0.05). • Values: Significant differences in the values reported { Vself, Vother, Vcompete,Vrawls, Vlower bound,Vchance}

19. Prediction of offers • Accuracy of prediction

20. Prediction of Culture

21. Conclusion *** Our results are consistent with reported results in the literature. • Opponent’s effect: People from US and India both treat virtual humans similar to how they would have treated another human. • We conclude that virtual humans can be a reasonable substitute to humans in online economic interactions. (eg. Selling and negotiation) • Culture’s effect: Values held by people from the two countries are different under similar conditions and the reasons should be further investigated. • Game’s effect:The most prominent cause affecting the game behavior and the offer values is the type of the game being played.

22. Future Work • More data collection • More cultures • More types of games (potential for other values to be distinguished) • Modeling • Culture-specific agent models based on reported values • Correlations between Hoftstedequestions/dimensions and values (for cultures with no values data reported)

23. Thank you!  • Questions?