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Decision(neuro)science
- 1. Decision (Neuro)Science in Society Rei Akaishi Unit Leader at RIKEN CBS Machine Learning Tokyo at U Tokyo
- 5. Satisficing (Suffice + Satisfactory) "decision makers can satisfice either by finding optimum solutions for a simplified world, or by finding satisfactory solutions for a more realistic world. Neither approach, in general, dominates the other, and both have continued to co-exist in the world of management science." https://www.wikiberal.org/wiki/Herbert_A._Simon Simon, 1979
- 6. Decision
- 7. AI・MLの仕事は経営の意思決定の仕事 Jobs of AI/ML are decision making in management. Kazuo Yano, Hitachi Cassie Kozyrkov, Google Data are beautiful, but it s decisions that are important. It s through our decisions ̶ our actions ̶ that we affect the world around us. https://towardsdatascience.com/introduction-to-decision-intelligence-5d147ddab767
- 9. Who are we?
- 10. Homo Economicus • Rational • Unbounded Capacity • No Cognitive Bias • Only Self Interest • No Interest in Others https://mises.org/blog/homo-economicus-straw-man
- 12. Rational, Optimal & Adaptive • Rational • optimal for a purpose of achieving a certain specific goal. • Optimal • maximally efficient for achieving a certain specific goal • Adaptive • compatible and effective (to a certain extent) for a purpose of achieving a certain specific goal.
- 16. ol and Grit Goal Goal Goal Goal GoalGoalGoal GoalGoal Goal Action Action Action Action Action Action Action Fig. 1. Hierarchical goal framework. Goals are typically organized hierarchically, with fewer high-level goals and more numerous low-level goals; the latter are associated with action tendencies, here broadly construed to include attention, emotion, and behavior. Duckworth & Gross, 2014
- 18. Summary • Goals are important for any types of decision making. • Without goal, rationality, optimality, and adaptiveness cannot exist. • It is important to set right set of goals for both biological and artificial systems. • Humans are good at setting (hierarchical) goals.
- 20. 2/23/18, 2:40 PMMistletoe株式会社 to Expo '70 Commemorative Park - Google Maps Map data ©2018 Google, ZENRIN 20 mi Japan, 〒107-0061 Tokyo, Minato, Kitaaoyama, 2 Chome−9−5 Mistletoe株式会社 Get on 首都高速3号渋谷線 in 世田谷区 ３丁目 from 国道246号線 12 min (4.3 km) Drive 483 km, 5 h 43 minMistletoe株式会社 to Expo '70 Commemorative Park
- 21. Skill, Habit and Control Initial state Estimates drift with noise Heading estimate updated Distance vector updated Lane error grows Speed error grows A B 5 4 3 2 1 0 –1 –2 –3 2.0 1.5 –1.5 0.5 –0.5 1.0 –1.0 0.0 SpeedFollow 3 2 ble and un- odules. Follow mod- e necessary auses them ward uncer- w module is mproves its odules’ state ogression of odules: con- er following nes indicate odule’s rele- hus, for the ts the car’s depicts the he lead car, the angle to ates overlap s low uncer- iverge, mak- uncertainty. mate updated Distance vector updated Lane error grows Speed error grows Hayhoe & Ballard, 2014
- 22. Hierarchical Reinforcement Learning A CB An illustration of how options can facilitate search. (A) A search tree with arrows indicating the pathway to a goal state. A specific sequence ndently selected actions is required to reach the goal. (B) The same tree and trajectory, the colors indicating that the first four and the la have been aggregated into options. Here, the goal state is reached after only two independent choices (selection of the options). (C) Illustr using option models, which allow the ultimate consequences of an option to be forecast without requiring consideration of the lower-level st be involved in executing the option. (For interpretation of the references to color in this figure legend, the reader is referred to the web versio ) M.M. Botvinick et al. / Cognition 113 (2009) 262–280 Botvinick & Niv et al., 2009
- 24. Value and opportunity cost in a current environment Value and opportunity cost in another environment Kolling et al., Science, 2012 Hayden et al., Nature Neuroscience, 2011 Kolling et al., Neuron, 2014 Wittmann, Kolling, Akaishi et al., Nature Communications, 2016
- 25. Summary • Everyday behaviors like driving a car consist of individual actions and a chunk of these individual actions. • The behavioral hierarchy is critical to organize our actions. • Recent developments in decision neuroscience, hierarchical reinforcement learning and foraging decision, capture this hierarchical organization of human behavior.
- 27. • Dunbar number suggests that a single person can have 100-200 people of personal connections in her/his social network. • Yet, our society consists of much larger number of people. • There has to be something in human cognition that can overcome the limitation of Dunbar number. Dunbar Harari
- 28. • Cooperative relationships and social norms can be created by the human beings living in groups through the means of punishment and establishing trust. Hardin Ostrom Yamagishi (山岸) • The ideal decision makers, Homo Economicus, creates the situation ‘Trajedy of Commons’. • If this is true for real human beings, a human community cannot survive by sharing common resources. • But actual human beings and their community (at least for those who have actually survived) can share goals and run organizations.
- 29. George Mason They (western states) will have the same pride, and other passions, which we (eastern states) have; and will either not unite with, or will speedily revolt from, the Union, if they are not in all respects placed on an equal footing with their brethren. —at 1787 constitutional convention.
- 31. Inequality Aversion • One player, the dictator, has a sum of money which he can allocate between himself and another player, the recipient. • The Dictator game measures a positive concern for the recipient s material payoff that is independent of the recipient s behavior, because the recipient has no actions to take. • Dictator allocations are found to be a mixture of 50% offers and 0% offers, and a few offers in between. left to right, arcs represent the possible actions that the player could take, and dotted lines represent one exam- ple choice. The numbers represent the material payoffs for the players and they are color coded (along with the actions) to match the players. For example, in the depic- tion of the Dictator game (see Figure Box 11.1), the dicta- tor (red) has $10 and can send any of that $10 to the recipient (blue). In this particular example, the dictator sends $3 to the recipient, keeping $7 for himself. Fischbacher, 2004). In Figure Box 11.3, we assume that the third party has an endowment of $5 and for every dollar spent on punishment the dictator loses a dollar. In the example, the third party spends $3 on punish- ment, reducing his payoff from $5 to $2 and reducing Send 3 Dictator Recipient 7, 3 10, 0 0, 10 FIGURE BOX 11.1 Example of a Dictator game. Offer 3 Proposer Offer 0 Offer 10 Responder Accept Reject 7, 0, FIGURE BOX 11.2 Example of an Ultimatum game. NEUROECONOMICS Fehr 5 Ui(x) = xi - αi 1 n −1 max j ≠ i ∑ {xj - xi,0} -βi 1 n − 1 max j ≠ i ∑ {xi - xj,0} , we assume βi ≤ αi and 0 ≤ βi < 1. In the two-player case (1) simplifies to Fehr & Schmidt, 1999
- 32. Eat Like Locals (Social Influence) https://www.youtube.com/ watch?v=FldlObA4Cdw https://www.youtube.com/ watch?v=sMZR-YGz_Gc
- 34. Envy & Schadenfreude • Schadenfreude: a positive emotional state in the face of someone else s misfortune • Envy: a negative emotional state in the face of another s fortune http://comments.bmartin.cc/2014/11/12/feeling- good-when-others-suffer/
- 35. Trust
- 38. 信頼 安心
- 39. https://www.cnn.com/2016/03/28/politics/hillary-clinton-trump-supreme-court/index.html Brady et al., PNAS, 2017
- 42. Summary • There are increasing demands for understanding humans in the context of social networks. • Humans usually do no like inequality. Successful civilizations have considered this value systems of humans well. • Trust is the major issue in both industries and societies globally. • But the nature of the issues regarding trust may differ across different cultures and countries.
- 43. Thanks for your attention!