Name |
Abbreviation |
Description |
Unconditional Cooperator |
Cu |
Cooperates unconditionally. |
Unconditional Defector |
Du |
Defects unconditionally. |
Random |
Random (=C.5 or
R(.5,.5,.5) or S(.5,.5,.5,.5)
below) |
Cooperates with probability one-half. |
Probability p Cooperator |
Cp for \(0\le\)p\(\le 1\) |
Cooperates with fixed probably \(p\). |
Tit for Tat |
TFT (=R(1,1,0) or
S(1,0,1,0) below) |
Cooperates on the first round and imitates its opponent’s
previous move thereafter. |
Suspicious Tit for Tat |
STFT (=R(0,1,0) below) |
Defects on the first round and imitates its opponent’s
previous move thereafter. |
Generous Tit for Tat |
GTFT (=R(1,1,g(R,P,T,S))
below) |
Cooprates on the first round and after its opponent cooperates.
Following a defection, it cooperates with probability \(g(R, P, T, S)=
\min\{1-\frac{T-R}{R-S}, \frac{R-P}{T-P}\}\), where \(R,\) \(P,\)
\(T\) and \(S\) are the reward, punishment, temptation and sucker
payoffs. |
Gradual Tit for Tat |
GrdTFT |
TFT with two differences: (1) it increases the string of
punishing defection responses with each additional defection by its
opponent (2) it apologizes for each string of defections by
cooperating in the subsequent two rounds. |
Imperfect TFT |
ImpTFT |
Imitates opponent’s last move with high (but less than
one) probability. |
Tit for Two Tats |
TFTT (or TF2T) |
Cooperates unless defected against twice in a row. |
Two Tits for Tat |
TTFT (or 2TFT) |
Defects twice after being defected against, otherwise
cooperates. |
Omega Tit for Tat |
ΩTFT |
Plays TFT unless measures of deadlock or randomness exceed
specified thresholds. When deadlock threshold is exceeded it
cooperates and resets the measure. When randomness threshold is
exceded, it switches to unconditional defection. For full
specificiation see Slaney and Kienreich, p184.
ΩTFT finished second in the 2005 reprise of the
Axelrod IPD tournament. |
GRIM (or TRIGGER) |
GRIM (= S(1,0,0,0) below) |
Cooperates until its opponent has defected once, and then
defects for the rest of the game. |
Discriminating Altruist |
DA |
In the Optional IPD, cooperates with any player that has never
defected against it, and otherwise refuses to engage. |
Pavlov (or Win-stay, Lose-shift) |
WSLS ( =P1
below) |
Cooperates if it and its opponent moved alike in previous move
and defects if they moved differently. |
n-Pavlov |
Pn |
Adjusts its probability of cooperation in units of
\(\tfrac{1}{n}\) according to its payoff on the previous round. More
specifically it cooperates with probability \(p_1=1\) on round 1 and
probability \(p_{n+1}\) on round \(n+1\), where
\(p_{n+1}=\) |
\(p_n\,[+]\tfrac{1}{n}\) if payoff on last round was Reward
\((R)\), |
|
\(p_n\,[-]\tfrac{1}{n}\) if payoff on last round was Punishment
\((P)\), |
|
\(p_n\,[+]\tfrac{2}{n}\) if payoff on last round was Temptation
\((T)\), |
|
\(p_n\,[-]\tfrac{2}{n}\) if payoff on last round was Sucker
\((S)\). |
\(p_n\) is the probability of cooperation on round n, \(x[+]y
= min(x+y,1)\) and x[-]y=max(x-y,0). |
Adaptive Pavlov |
APavlov |
Employs TFT for the first six rounds, places
opponent into one of five categories according to its responses and
plays an optimal strategy for each. Details described in Li pp 89-104.
APavolv was the highest scoring strategy in the 2005
reprise of Axelrod’s IPD tournament. |
Reactive (with parameters y,p,q) |
R(y,p,q) |
Cooperates with probability y in first round and with
probabilities p or q after opponent cooperates or defects. |
Memory-one (with parameters p,q,r,s) |
S(p,q,r,s) |
Cooperates with probabilities probabilities p,q,r or s after
outcomes (C,C), (C,D), (D,C) or (C,D). |
Zero Determinant |
ZD |
A class of memory-one strategies that guarantee that a
player’s long-term average payoff in the infinitely repeated,
two-player prisoner’s dilemma (2IPD) will be related to his
opponent’s according to a fixed linear equation. |
Equalizer (or dictator) |
SET-n (for P≤n≤R) |
A ZD strategy that guarantees the
opponent’s long term average payoff is n. As it turns out, in a
PD with payoffs 5,3,1 and 0,
SET-2=S(¾¼½¼). |
Extortionary |
Extort-n |
An extortionary strategy is a ZD strategy that
guarantees that an opponent’s average payoff can exceed the
punishment payoff only if one’s own long term average payoff is
greater. Extort-n guarantees that one’s gain
over punishment is n times one’s opponent’s. As it turns
out, for a PD with the payoffs above,
EXTORT-2=S(7⁄8,
7⁄16,3⁄8,0)).
|
Generous |
Gen-n |
A generous strategy is a ZD strategy that
guarantees that an opponent’s average payoff can be lower than
the reward payoff only if one’s own long term average payoff is
even lower. GEN-n guarantees that one’s loss
relative to the reward is n times one’s opponent’s. As it
turns out, for a PD with the payoffs above,
GEN-2=S(1,
9⁄16,1⁄2,1⁄8)).
|
Good |
GOOD |
A good strategy for the infinitely-repeated, two-player PD is a
strategy with the following properties: (1)its use by both players
ensures that each gets reward as long-term average payoff, (2)it is a
nash-equilibrium with itself, and (3)if it is employed by both, any
deviation by one that reduces the average payoff of the other will
also reduce its own average payoff. Aikin, 2013 provides a simple
characterization of the memory-one strategies that are good. |
Lookup tables |
LookerUp |
LookerUp is a family of strategy-archetypes.
For positive integers k,m,n a LookerUp strategy can be obtained by
using a learning algorithm to calculate the best reponse to the
opponents first k moves and last m moves and the player’s own
last n moves while playing a PD-tournament within some universe of
strategies. |
Neural Net |
ANN |
A neural net strategy is obtained by using a learning algorithm
to set connection weights on a feed-forward neural network with one
hidden layer while playing PD tournaments within some universe of
strategies. Further details can be found in Harper et al. |
Finite State Machine |
FSM |
A finite state machine strategy is obtained by using a learning
algorithm to set PD-moves and state-transitions while playing PD
tournaments within some universe of strategies. Further details can be
found in Harper et al. |