I am looking for a way to simulate behavioral economic models to illustrate the effects of changing parameters. I am familiar with statistical analysis (especially R), but I only have few programming skills beyond R. I think that if there is a way to achieve my goal (I'm not really sure if it is possible at all!) that Octave should be a good choice.Usually such a model defines some kind of game with actions, strategies and payoffs for every player. Then these actions get evaluated and normally an equilibrium between the players actions/strategies is determined.One of my desired models is Rabin's idea of Fairness Equilibria. Pages 1286ff can give a short idea of how Rabin defines the game and determines the equilibrium. LINKMy goal is to have a function that depicts that model and enables me to illustrate the effects of different assumptions and situations within the frame of the specific model.So my question is: Is it possible at all? Is Octave suitable or should I choose another language/environment?Thanks in advance for any sort of advice!EDIT:I try to sketch the model:Starting point is a two player normal form game (Prisoner's Dilemma) with the strategy sets $S_1, S_2$ for every player from which they choose their actions. The payoff function for each player is defined as $\pi_i = S_1 \times S_2$. So player $i$ chooses action $a_i \in S_i$. Now player $j$ has a believe about $i$'s strategy $b_i \in S_i$ and player $i$ has a belief about player $j$'s belief about $i$'s strategy $c_i \in S_i$. So the feasible payoffs for $i$ depending on his beliefs are in the set $\Pi(b_j) = \{(\pi_i(a_i,b_j),\pi_j(b_j,a_i)) | a_i \in S_i\}$That's the framework.The routine now has to determine$\pi_j^h(b_j)$ as the highest payoff for $j$ depending on $i$'s belief that is Pareto efficient in $\Pi(b_j)$$\pi_j^l(b_j)$ as the lowest payoff for $j$ depending on $i$'s belief that is Pareto efficient in $\Pi(b_j)$$\pi_j^{min}(b_j)$ as the worst possible payoff for $j$ depending on $i$'s beliefthe reference point for $j$: $\pi_j^e(b_j) = \frac{\pi_j^h(b_j) + \pi_j^l(b_j)}{2}$the kindness of $i$ towards $j$: $\kappa_i(a_i,b_j)=\frac{\pi_j(b_j,a_i) - \pi_j^e(b_j)}{\pi_j^h(b_j) - \pi_j^{min}(b_j)}$believed kindness of $j$ towards $i$: $\bar\kappa_j(b_j,c_i)=\frac{\pi_i(c_i,b_j) - \pi_i^e(c_i)}{\pi_i^h(c_i) - \pi_i^{min}(c_i)}$Then the Expected Utility is defined as: $U_i(a_i,b_j,c_i) = \pi(a_i,b_j) + \bar\kappa_j(b_j,c_i) \cdot [1+\kappa_i(a_i,b_j)]$Now, to be a Fairness Equilibrium, a pair of strategies $(a_1^*,a_2^*)$ has to fulfill following conditions:$a_i^* \in \text{argmax}_{a_i \in S_i} U_i(a_i,b_j,c_i)$$c_i = b_j = a_i^*$So my goal is to get a routine that determines these Fairness Equilibria when a certain game is given.
Software to simulate behavioral economic models?
simulation;octave