content_image/321051.jpg content_image/321052.jpg
a constant η$_{i}$ with larger values signifying increasing influence from the other group members relative to one’s own position and hence lower confidence. We take η$_{i}$ to depend linearly on the initial wager w 0 i as follows: 

<img src='content_image/321051.jpg'>

so that a person who bets $0 has η$_{i}$ = η$_{max}$ and is least resistant to persuasion whereas a maximum $7 wager implies highest confidence with η$_{i}$ = η$_{min}$ . 

The network weights b$_{ij}$ are then defined to integrate susceptibility and the nor- malized communication weights ˜ ν$_{ij}$ as follows: 

<img src='content_image/321052.jpg'>

where ∑ N j =1 ˜ ν$_{ij}$ = 1 (˜ ν$_{ii}$ = 0). The unnormalized communication weights ν$_{ij}$ are taken to be the same as used in the RPM and ASC simulations. 

Without loss of generality, we set η$_{max}$ = 1 (equivalent to the rescaling t → η$_{max}$t , which simply changes how fast equilibrium is reached, not the equilibrium values themselves). This leaves η$_{min}$ as the free parameter. Normative-rounding is employed. The total χ 2 is minimized at χ$_{min}$ = 0 . 45. Figure S4 displays the results. The one- parameter χ 2 goodness-of-fit test for the Abelson model with wager-based confidence yields Q = 0 . 009 for χ 2 = 25 . 0. 

## Abelson Model with Extremist-Tilting Based on Intuitive Confidence 

This variant of the extremist-tilted Abelson model assumes that confidence increases not with the wager but based on the intuitive confidence. Therefore, the susceptibility to influence is taken to decrease as the subjective probability of game victory grows 

