An Adaptive Affective Social Decision Making Model Alexei Sharpanskykh Jan Treur vrije Universiteit amsterdam

2 Motivation Traditionally, human decision making has been modelled as the problem of rational choice from a number of options using economic utility-based theories Research Aim: To create a more biologically plausible model of human decision making based on theoretical principles from Neuroscience and Social Science

3 Decision Making Aspects Predicted effects of the options Valuing of these effects Emotions felt in relation to this valuing Social influence

4 Predicted effects of the options Simulated behavior and perception chains by Hesslow s1, s2, s3... are sensory states r1, r2, r3... are preparation states w1, w2,... are link strengths V1, V2,... are state values w1 w2 V1 V2

5 Predicted effects of the options Simulated behavior and perception chains by Hesslow

6 Emotion generation As if body loop (by A.Damasio) sensory state preparation for the induced bodily response sensory representation of the bodily response induced feeling

7 Emotions and Valuing Decision making involves emotional valuing of predicted consequences of decision options A notion of value, involving emotions is represented in the amygdala Damasios Somatic Marker Hypothesis Each represented decision option induces (via an emotional response) a feeling which is used to mark the option

8 Social contagion As emotion state for option O As intention state for option O emotion states of other group members for option O intention states of other group members for option O As somatic marking for option O As mirroring of emotion for option O As mirroring of intention for option O

9 Learning Hebbian learning principle: connections between neurons that are activated simultaneously are strengthened action-effect prediction links emotion-related valuation links social influence links d (r1, s2)/dt = r1s2 (1 – (r1, s2)) – (r1, s2)

10 Learning (I) action-effect prediction links (II) emotion-related valuation links (III) social influence links Learning of links (II) has the greatest impact on decision making Learning of links (III) has a negligible effect on decision making, when agents are similar A combination of learning of all links (I), (II) and (III) results in the strongest discrimination between the options

11 Combined model

12 Simulation Results The path is short, but becomes dangerous The path is average, but dangerous The path is long, but safe time # action Preparation for the execution of the options

The developed model could be used to evaluate and predict emotional decisions of individuals in groups under stressful conditions Learning of the emotion-related links has the strongest effect on discrimination of decision making options (cf. the role of the Amygdala in valuing) In the future emotion regulation mechanisms (e.g., to cope with fear and stress) will be investigated Conclusions