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Active Inference in Multiagent Systems 81

Using these generated datasets, we conducted several evaluations, com-
paring the discrete decision-making model (Rivkin & Siggelkow, 2003)
with two alternative decision processes: perception-maximizing decisions
(selecting decisions as a maximum of the max-marginal probabilities), and
energy minimization (selecting a decision by sampling max-marginals to
minimize surprise). We also analyzed the impact a specific team structure
(models) had on the ability of the team to find correct solutions.

4.4.2 Discrete Decision Making Versus Free Energy

In our first evaluation, we compared the performance of distributed discrete
decision making (D3M) heuristics with our model, where the decision vec-
tor minimizes a free-energy function. Fig. 4.6 shows the normalized payoff
achieved at the 100th iteration by the best of D3M policies (Rivkin & Sig-
gelkow, 2003) versus payoffs of one of the teams defined by our energy-
optimizing model. While the free-energy solution provides only a marginal
improvement compared to the D3M model (2%–10%), our model achieves
convergence much faster than the D3M heuristics (usually at 15 iterations
versus 50–80 iterations for D3M), and maintains high convergence for
increasing objective function complexity (parameter K), while the perfor-
mance of D3M consistently decreases with K. As a result, we continued
to compare only energy-based adaptation processes.
In the next set of experiments we analyzed the ability of a team to adapt
to changes in the environment, defined as random regeneration of an objec-
tive function’s parameters (without changing the topology of the factor
graph) introduced at every 20 decision iterations.

4.4.3 Impact of Agent Network Structure

We compared the effects of three different agent network topologies
(Fig. 4.7) on the quality of the search process and the ability of a team to

1
Normalized payoff @ iteration 100 0.96 D3M
0.98

0.94
FreeEnergy
0.92
0.9
1 2 3 4 5
K (number of neighbors)
Fig. 4.6 Comparing performance of the D3M heuristic versus free-energy minimization.