Arianna Dal Forno and Ugo Merlone (2002)
A multi-agent simulation platform for modeling perfectly rational and bounded-rational agents in organizations
Journal of Artificial Societies and Social Simulation
vol. 5, no. 2
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Received: 23-Dec-2001 Accepted: 20-Feb-2002 Published: 31-Mar-2002
where ei is agent i's effort. This formulation satisfies the common assumptions we stated.
Figure 1. The main window with controls for the simulation parameters |
Figure 2. The toroidal grid with agents (different colours representing different types and behaviours of agents) |
Figure 3. Efforts window with agents' effort displayed with a chromatic scale |
Figure 4. General structure of the simulation |
Initially, according to world parameters (chosen by user) agents are randomly distributed on the grid. Each agent's initial effort is determined accordingly to their type; for most types of agents initial effort is determined randomly. Agents' position and effort may be observed on the relative display window. At every round each agent moves randomly in order to meet a partner and form a team. The only constraint on possible movement is that no more than one agent may stay in a cell of the grid.
class Agent { public: Agent(int _type = 0 ); ~Agent(); void RandAgent(int* grid, int modx, int mody, int num); void RandMove(int* grid, int modx, int mody); void WorkAgent(float parteff); void RandDir(); protected: private: int type; // type of agent int posx; // x position on the grid int posy; // y position on the grid int dir; // direction N=1, E=2, S=3, W=4, No Interact=0 int step; // allowed motion int col; // effort colour int colt; // type colour int evol; // the type of evolution the agent has reached int numinc; // number of interactions; double effort; // current effort double profit; // current profit double cumprof; // cumulated profit double leffort; // last effort provided by agent double cumeffort; // cumulated partner effort double lprofit; // last profit double aeffort; // last antagonist effort double aprofit; // last antagonist profit double neffort; // neighbourhood average effort double nprofit; // neighbourhood average profit };
Figure 5. Local effects prevent the emergence of a corporate culture |
Table 1: Average effort introducing high effort agents in a population of null effort agents | ||||||
% High Effort | 0.0% | 0.6% | 5.6% | 33.3% | 66.7% | 100.0% |
Expected Effort | 0.00010 | 0.01122 | 0.11126 | 0.66707 | 1.33403 | 2.00100 |
Observed Effort | 0.00010 | 0.01092 | 0.12283 | 0.67948 | 1.32605 | 2.00100 |
Table 2: Average effort introducing high effort agents in a population of shrinking effort agents | ||||||
% High Effort | 0.0% | 0.6% | 5.6% | 33.3% | 66.7% | 100.0% |
Expected Effort | 0.00010 | 0.01122 | 0.11126 | 0.66707 | 1.33403 | 2.00100 |
Observed Effort | 0.00010 | 0.01235 | 0.20056 | 0.98380 | 1.58420 | 2.00100 |
Figure 6. The effect of high effort agents on bounded rationality agents |
Table 3: Average effort introducing high effort agents into a population of rational agents | ||||||
% High Effort | 0.0% | 0.6% | 5.6% | 33.3% | 66.7% | 100.0% |
Expected Effort | 0.92101 | 0.92701 | 0.98101 | 1.28100 | 1.64100 | 2.00100 |
Observed Effort | 0.92101 | 0.92823 | 0.97348 | 1.25872 | 1.59225 | 2.00100 |
Figure 7. The effect of high effort agents on rational agents |
Figure 8. Effects of noise on a simple population |
Figure 9. Noise effects on a winner imitators population |
Figure 10. Rational agents shirk when interacting with high effort agents |
2 Obviously, it is possible to implement a different profit function.
3 The .exe program and the agent class code is available upon request from the authors.
4 The reader will note that in our model interaction depends on the location of agents. The principal may mitigate the random interaction effects by an optimal location of agents. But this cannot be done without knowing the agents' types: a vicious circle arises.
5 For numerical reasons null effort agents actually exert an almost null effort: 0.0001.
6 The only exception is a rational agents population. See Dal Forno and Merlone (2001) for theoretical details and 6.16.
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