Stéphane Airiau, Sabyasachi Saha and Sandip Sen (2007)
Evolutionary TournamentBased Comparison of Learning and NonLearning Algorithms for Iterated Games
Journal of Artificial Societies and Social Simulation
vol. 10, no. 3 7
<https://www.jasss.org/10/3/7.html>
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Received: 06Dec2005 Accepted: 09Jun2007 Published: 30Jun2007
Figure 1. Modified Tournament Selection Algorithm for a population of N agents 
Zoom around iteration 100  

All iterations  Zoom around iteration 500 
Figure 2. Choice of the number of iterations 
Table 1: Algorithm ranking based on roundrobin competition with one player per algorithm  
Rank  Avg Score  Algorithm  std 

1  FP  3.014  0.002 
2  Saby  3.003  0.004 
3  BR  2.951  0.003 
BRFP  2.949  0.000  
5  Bully  2.939  0.000 
6  Nash  2.906  0.000 
7  M  2.768  0.000 
8  GTFT  2.751  0.008 
9  R  2.388  0.001 
Table 2: Head to head results  mean of the score over all interactions, obtained by the row player in the table, while playing against the column player in the table.  
Rank  Avg Score  Algorithm  FP  Saby  BR  BRFP  Bully  Nash  M  GTFT  R 
1  3.014  FP  2.950  2.950  3.027  2.999  3.081  2.943  3.081  3.101  2.994 
2  3.003  Saby  3.090  2.986  3.040  2.934  3.080  2.929  3.079  2.989  2.904 
3  2.951  BR  3.016  2.995  2.901  2.919  3.081  2.931  3.081  2.720  2.910 
4  2.949  BRFP  3.040  3.122  3.133  2.906  2.797  2.921  2.884  2.858  2.879 
5  2.939  Bully  3.116  3.116  3.116  2.871  2.696  2.891  2.783  2.976  2.888 
6  2.906  Nash  2.908  2.930  2.926  2.930  2.811  2.933  2.898  2.922  2.899 
7  2.768  M  2.696  2.698  2.696  2.713  2.626  2.734  2.626  3.134  2.994 
8  2.751  GTFT  2.728  2.921  2.830  2.880  2.880  2.932  2.582  2.510  2.495 
9  2.388  R  2.375  2.391  2.402  2.355  2.311  2.363  2.310  2.494  2.494 
Table 3: Headtohead performance  net score difference of the row player in the table when playing against the column player in the table  
Algorithm  FP  Saby  BR  BRFP  Bully  Nash  M  GTFT  R 
FP  0.140  0.011  0.040  0.035  0.035  0.385  0.373  0.618  
Saby  0.140  0.044  0.188  0.035  0.001  0.380  0.067  0.513  
BR  0.011  0.044  0.214  0.035  0.004  0.385  0.110  0.508  
BRFP  0.040  0.188  0.214  0.074  0.009  0.170  0.021  0.524  
Bully  0.035  0.035  0.035  0.074  0.080  0.157  0.096  0.577  
Nash  0.035  0.001  0.004  0.009  0.080  0.163  0.010  0.535  
M  0.385  0.380  0.385  0.170  0.157  0.163  0.551  0.683  
GTFT  0.373  0.067  0.110  0.021  0.096  0.010  0.551  0.000  
R  0.618  0.513  0.508  0.524  0.577  0.535  0.683  0.000  
Table 4: Head to head results  standard deviation of the score obtained by the row player in the table while playing against the column player in the table (the value of the standard deviation is the value of an entry times 10^{3})  
Algorithm  FP  Saby  BR  BRFP  Bully  Nash  M  GTFT  R 
FP  0.00  0.21  0.00  0.00  0.00  0.50  0.00  6.34  0.42 
Saby  0.20  2.69  3.04  0.18  0.00  0.36  0.02  0.29  0.59 
BR  0.00  3.15  0.00  0.00  0.00  0.41  0.00  9.69  0.49 
BRFP  0.00  0.22  0.00  0.00  0.00  0.34  0.00  0.00  0.39 
Bully  0.00  0.00  0.00  0.00  0.00  0.36  0.00  0.00  0.47 
Nash  1.55  0.40  0.39  0.29  0.24  0.34  0.21  0.53  0.53 
M  0.00  0.02  0.00  0.00  0.00  0.50  0.00  0.00  0.73 
GTFT  9.28  8.03  6.36  0.00  0.00  0.35  0.00  0.56  0.53 
R  1.92  0.62  0.49  0.45  0.52  0.77  0.45  0.51  0.50 
Figure 3. Evolutionary Tournament with 5 algorithms (1% Nash and equal proportion of R, GTFT, BR and M) with tournament selection (left) and modified tournament selection (right) 
There are two important observations from this representative population:
Figure 4. Evolutionary Tournament with 6 algorithms (1% FP and equiproportion of R, GTFT, BR, MaxMin, Nash each) with tournament selection (left) and modified tournament selection (right) 
Figure 5. Evolutionary Tournament with 8 algorithms (1000 each of R, GTFT, BR, M, Nash, FP, BRFP, Saby), using modified tournament selection 
Tournament Selection  Modified Tournament Selection  
No Bully initially present  
Small proportion of Bully initially present 
Figure 6: Evolutionary tournaments including learning algorithms with or without Bully
(a) Modified tournament selection  (b) 99% Modified tournament selection and 1% random selection

(a) Tournament selection  (b) 99% Tournament selection and 1% random selection

Figure 9. Fitness Proportionate Selection with all algorithms 
^{1} A conflicted game is a game where no joint action can provide the most preferred outcome to all players.
^{2} For a given game in the testbed, there exists structurally equivalent games (that can be obtained by renaming the actions or the players), but none of the equivalent games is present in the testbed. All the games in the testbed are therefore structurally distinct. For each game structure, our selection of the representing matrices has introduced a bias: playing as a column player introduces an advantage.
^{3} Games with multiple Nash equilibria, rational agents will face a problem of equilibrium selection. Since the agents do not communicate, they may often select different equilibria and hence reach an undesirable outcome. A simple example is the coordination game, where each of the two agents gets 1 if they play the same action and 0 otherwise. There are three Nash equilibria, two pure strategies and one mixed strategy. One pure strategy equilibrium is when both players play the first action and other pure strategy equilibrium is when both players play the second action. In the mixed equilibrium, both the players play each action with probability 0.5. Therefore there is a significant chance that both players end up playing different actions and get 0.
^{4}When there are two algorithms i and j present in the population, we can easily compute the score obtained by any agent as a function of the proportion f_{i} of algorithm i in the population (1f_{i} is the proportion of algorithm j in the population). From Table 2, let h_{ij} denote the score obtained by an agent using algorithm i when playing against an agent using algorithm j. The score of an agent using algorithm i is s_{i}(f_{i})=f_{i}·h_{ii}+(1f_{i})·h_{ij} and the score for agent using algorithm j is s_{j}(f_{i}) = f_{i}·h_{ji}+(1f_{i})·h_{jj}. If s_{i}(f_{i})>s_{j}(f_{i}) or s_{i}(f_{i})<s_{j}(f_{i}) for all f_{i}∈[0,1], one algorithm will be ultimately used by all agents in the population. Otherwise, there exists a fixed point f_{i}^{*} where the two algorithms coexist: ∃f_{i}^{*}∈[0,1]  s_{i}(f_{i}^{*})=s_{j}(f_{i}^{*}): all the agents have the same score. Let us consider the case where s_{i}(f_{i}) is a decreasing function of f_{i} (s_{j}(f_{i})=1s_{i}(f_{i}) is then an increasing function) and we have ∀f_{i} < f_{i}^{*}, s_{i}(f_{i})>s_{j}(f_{i}) and ∀f_{i} > f_{i}^{*}, s_{i}(f_{i})<s_{j}(f_{i}). For f_{i} < f_{i}^{*}, the number of agents using algorithm i increases since those agents have better performance. However, with the increase in the number of agents using i, the performance of agents using i decreases whereas the performance of agents using algorithm j increases. When f_{i} >f_{i}^{*}, agents using the algorithm j outperform those using algorithm i and hence, the proportion of algorithm j now increases. This process repeats until the population reaches the equilibrium point. This shows that the fixed point is an attractor. Similarly, a theoretical equilibrium can be calculated for n strategies by solving the following problem: ∀ (i,j) r_{i} = r_{j} where r_{i}=Σ_{j} f_{j} h_{ij}, Σ_{i}f_{i}=1 and f_{i} ∈ [0,1] ∀i.
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