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Emerging strategies when facing uncertain1es:
Mr Banks game as a case study Josep Perello, Mario Gu1érrez-‐Roig, Jordi Duch
Josep.perello@ub.edu @JosPerello @OpenSystemsUB / @CLabBarcelona
2nd Annual Workshop on Complex Sociotechnical Systems, Valencia, June 09 2016
Outline
1. Decision-‐making and informaMon flows
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Outline
1. Decision-‐making and informaMon flows 2. DefiniMon of an experiment
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Outline
1. Decision-‐making and informaMon flows 2. DefiniMon of an experiment 3. Data analysis and interpretaMon: behavioural
biases and strategies
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Outline
1. Decision-‐making and informaMon flows 2. DefiniMon of an experiment 3. Data analysis and interpretaMon: behavioural
biases and strategies 4. Discussion
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What is the decision mechanism? Up or down?
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What is the decision mechanism? Up or down?
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What is the decision mechanism?
• SMmulus-‐Response-‐Outcome background
Some phenomena to be observed: • Unintended strategies • Behavioural biases
Up or down?
outcom
e
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changes
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A fesMval as a lab
• Barcelona. Board Game FesMval DAU, December 2013 • Experiment is also repeated in Brussels. CAPS meeMng,
July 2015
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ScienMfic results • 283 volunteers (35% females, 22% did operate in market) • 18,436 valid decisions • 44,703 clicks h`p://mr-‐banks.net
Mario GuMérrez-‐Roig, Carlota Segura, Jordi Duch, Josep Perelló Market Imita+on and Win-‐Stay Lose-‐Shi6 strategies emerge as unintended pa:erns in market direc+on guesses arXiv:1604.01557
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Slope 1.96
Time and informaMon
Men consult more informaMon than women. Adults consult more informaMon than kids.
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Expert advice
ParMcipants trusted the expert with 0.69 probability Expert was correct only in 60% of total acMons.
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Performance, opMmisMc bias and repeMMveness
• Global success ra1o: 0.536. Bullish 0.550, Flat 0.533, Bearish 0.503. No age, no gender differences.
• Probability to choose “up” is 0.606 (market probability 0.533). Op1mis1c bias.
• Probability to repeat same decision is 0.561 (market repeat 0.536). ExcepMon: kids are more inconstant (0.491).
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Emerging strategies • Market Imita1on. AutomaMc
imitaMon and common product of Bounded RaMonality. For instance: Rock-‐Scissors game (Cook etal. Proc Royal Society B, 2011). Mutual InformaMon 0.045(10) bits.
• Win-‐Stay Lose ShiQ. Common heurisMc learning strategy. For instance: Prisonner Dilemma (Nowak etal. Nature, 1998). Mutual InformaMon 0.050(10) bits.
Up or down?
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Emerging strategies • Market Imita1on. AutomaMc
imitaMon and common product of Bounded RaMonality. For instance: Rock-‐Scissors game (Cook etal. Proc Royal Society B, 2011). Mutual InformaMon 0.045(10) bits.
• Win-‐Stay Lose ShiQ. Common heurisMc learning strategy. For instance: Prisonner Dilemma (Nowak etal. Nature, 1998). Mutual InformaMon 0.050(10) bits.
Up or down?
outcom
e
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Market ImitaMon. Behavioural bias
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Market ImitaMon. Behavioural bias
Oveconfident behaviour
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Win-‐Stay Lose-‐Shil. Behavioural bias
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Win-‐Stay Lose-‐Shil. Behavioural bias
asymmetric risk
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Which is the dominant strategy?
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Which is the dominant strategy?
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Which is the dominant strategy?
CondiMonal Mutual InformaMon 0.05 condiMoned to previous outcome 0.07 condi1oned to previous market movement
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Emerging strategies aggregated
Coarse grain approach: Strategies when aggregated are iden1cal under binomial scenarios.
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Emerging strategies aggregated
• The less 1me the more prone to follow intuiMve strategies • The more info the more prone to follow intuiMve strategies • Women are more prone to follow the strategies • Kids have follow an hec1c behaviour (GuMérrez-‐Roig, Nat CommunicaMons,
2014) • Expert exogeneous signal mi1gates intuiMve strategies
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Robustness
CollecMve Awareness Planorms for Sustainability and Social InnovaMon conference linked Horizon 2020 EU research programme. Brussels, July 2015. It provides projects and iniMaMves with an opportunity to discuss their impact, and liaise with: civil society organisaMons, NGOs, local communiMes, students and hackers, academic and industrial insMtuMons, policy makers, naMonal agencies, new Members of the EU Parliament.
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Robustness
42 parMcipants. 2,372 decisions.
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Robustness
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Robustness
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Final remarks
• We build an out-‐of-‐the-‐lab experiment to study decision making mechanisms with a game
• We detect two intuiMve strategies: automaMc imitaMon, win-‐stay lose-‐shil
• Following emerging strategies does not seem to affect success raMo
• InformaMon over-‐flow reinforces intuiMve strategies
• Expert advice dissolves intuiMve strategies
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With the support of
Community of pracMce in CiMzen Science
CiMzen Science Office. Science Unit in the City Council
Science CommunicaMon in Bee-‐Path and Complexity Lab Barcelona (2014 SGR 608)
Mecánica estadísMca para “big data”: adquisición, análisis y modelización (FIS2013-‐47532-‐C3-‐2-‐P)
HosMng the experiments. Barcelona InsMtute of Culture
Josep.perello@ub.edu @JosPerello
@OpenSystemsUB @CLabBarcelona
Big thanks to: Isabelle Bonhoure, Mario GuMérrez-‐Roig, Jordi Duch, Inés Garriga, Nadala Fernández, Fran Iglesias, Pedro Lorente, Carlota Segura, Clàudia Payrató, Oscar Marín, Julian Vicens, and to volunteers.
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