This website contains three parallel-constraint satisfaction network models for non-strategic and strategic social decision-making, as described in the proposal Coherence-based Processes in (Non-)Strategic Social Decision-Making. You can modify the outcomes and social preferences in order to simulate the model and obtain predicted choice probabilities and decision times.
To select the model, you can use the tabs in the navigation bar. Please be aware that the functions have not been optimized yet, so the GUI may take a few seconds to load.
Non-strategic Social Decision-Making
Allocation Task with Two Options
This model predicts choices between two allocation options, as measured in the Ring Measure (Liebrand & McClintock, 1988). You can input the social preferences of the simulated person. That is, you can determine to what extent the person values their own payoff, the payoff to the other option, and how they weigh the difference in payoffs if they receive more or less than the other person. Additionally, you can input the payoffs for both options, both for the self and the other person. The parameter lambda, which determines how option node activations are translated into choice probabilities, can be set.
In the output, the network of the current situation is plotted. Green nodes represent positive activation, red nodes represent negative activation, and the thickness and color/shape of edges represent the strength and valence of weights for connections. The purple node(s) represent the option that the simulated person is predicted to choose. Under the tab Iterations you can also observe the distibution of activation for nodes of the middle and the output layer over model iterations.
Allocation Task with Nine Options
This model predicts choices among nine allocation options, as measured by the Slider Measure (Murphy et al., 2011). Unlike the model for allocation tasks with two options, choice probabilities and decision times are derived from model simulations. In each repetition of the simulation, a value is sampled from a normal distribution with a mean of 0 and a standard deviation (SD) that can be set. This sampled value is then added to each weight between the input and middle layers, which represent social preferences, in order to simulate noisy social preferences.
During each repetition, the option selected by the model and the number of iterations required to settle the spread of activations in the network are recorded as indicators for choices and time. Choice probabilities, the median number of iterations over simulation repetitions, and the distribution of model iterations can be observed under different tabs.
Strategic Social Decision-Making
Public Good with Three Players
This model predicts whether a participant contributes all, half, or no money in the public goods game as administered in a study by Fiedler et al. (2013). To simplify the illustration of the model, the two other players can contribute either all, half, or none of their money. All contributions to the public good are multiplied by a factor that can be set and distributed evenly among the three players. The payoff for each player is given by \(\text{Payoff}_{Self} = \text{Endowment}_{Self} - \text{Contribution}_{Self} + (\sum_{i = 1}^{I = 3} \text{Contribution}_i)/3\). The possible payoffs serve as the input in the input layer. Own and other endowments can be set, as well as the efficiency of the public good. Social preferences and expectations of cooperation can also be set. The model provides probabilities for the contribution of the simulated player (i.e., full, half, or none) under each possible condition of contribution from the other two players (i.e., full, half, none) and overall (i.e., unconditional probabilities). Please note that the contribution options have been limited to full, half, or none in order to provide a clearer illustration.
References
Fiedler, S., Glöckner, A., Nicklisch, A., & Dickert, S. (2013). Social value orientation and information search in social dilemmas: An eye-tracking analysis.
Organizational Behavior and Human Decision Processes,
120(2), 272–284.
https://doi.org/10.1016/j.obhdp.2012.07.002
Liebrand, W. B. G., & McClintock, C. G. (1988). The ring measure of social values: A computerized procedure for assessing individual differences in information processing and social value orientation.
European Journal of Personality,
2(3), 217–230.
https://doi.org/10.1002/per.2410020304
Murphy, R. O., Ackermann, K. A., & Handgraaf, M. (2011). Measuring social value orientation.
Judgment and Decision Making,
6(8), 771–781.
https://doi.org/10.2139/ssrn.1804189