A fresh look at “Christian Game” as a potentially winning arrangement.
Reader’s Note: In this post, game theory is about decision making strategies, not about the PUA kind of game, although they can and do overlap.
Length: 2,350 words
Reading Time: 8 minutes
This post is intended to let readers become more aware of game theories with the purpose of gaining an objective view towards important real-life applications.
Other posts in this series which you may want to read before continuing on.
- Σ Frame (Jack): Introduction to Game Theory 101 (2018 February 11).
- Σ Frame (Jack): Game Theory 110 — The Prisoner’s Game (2021 March 3)
- Σ Frame (NovaSeeker): Constructing a Framework of Options (2021 March 15)
Also, readers are encouraged to click on the many links contained in this post for further reading about game theory.
“Shall we attempt this one? Let’s do it!”Kevin Cronan (lead singer of the supergroup, REO Speedwagon)
The Evolution of Winning Strategies
In this section, “evolution” describes how two players who are initially unfamiliar with the other’s strategy will, upon multiple adaptive reiterations, eventually learn how to cooperate in a mutually beneficial fashion.
In a relationship, “success” or “winning” can be interpreted in two different ways.
- Gaining more “points” than the opponent. In real life, “points” could be interpreted as a wide number of things, including affirmation, attention, control, favor, love, loyalty, recognition, respect, and others.
- Achieving a mutually beneficial play strategy, characterized by various milestones, such as altruism, commitment, harmony, honor, humility, trust, and others.
Those who are young, emotionally energetic, and spiritually immature usually start off under the assumption of the first definition above. As time and experience leads to maturity, most people eventually come to appreciate the second definition.
Robert Axelrod used an algorithm to show a possible mechanism for the evolution of altruistic behavior from mechanisms that are initially purely selfish, by natural selection. It was later applied to the Prisoner’s Dilemma in order to model the evolution of altruism.
Axelrod discovered that when encounters were repeated over a long period of time with many players, each with different strategies, greedy strategies tended to do very poorly in the long run while more altruistic strategies did better, as judged purely by self-interest.
This is because, in reality, humans display a systemic bias towards cooperative behavior in socio-sexual games, much more so than predicted by simple models of “rational” self-interested action. This has been proven by alternate game simulations which adopted group-based motivations.
For example, one model based on a different kind of rationality, where people forecast how the game would be played if they formed coalitions and then they maximize their forecasts, has been shown to make better predictions of the rate of cooperation in the Prisoner’s Dilemma and similar games given only the payoffs of the game. In other words, group socialization coerces players to be honest (sooner or later) for the benefit of participating in the group.
So apparently, one’s initial degree of rational self-interest makes little difference in the long run of life; what matters is how one plays the game, and whether one learns to be cooperative. I believe this is where the failure in the current mating marketplace lies. A significant segment of the population is not playing the game (e.g. MGTOW, incels), and another large segment is not learning to be cooperative (e.g. PUAs, frivorcing wimminz).
2 Approaches to Develop an Optimal Strategy
Deriving the optimal strategy is generally done in two ways:
- The Empirical Approach: Monte Carlo simulations of populations have been made, where individuals with low scores die off, and those with high scores reproduce. Thus, a Genetic Algorithm sets forth a context for finding an optimal strategy, which is mimicked by evolutionary psychology models in the Red Pill lore. It results in either the “Brute Force” or the “Capitulation” options described in NovaSeeker’s post. We could also call this “The Law of the Jungle”. The mix of algorithms in the final population generally depends on the mix in the initial population. The introduction of mutation (random variation during reproduction) lessens the dependency on the initial population. Empirical experiments with such systems tend to produce Tit for Tat players (described below), but no analytic proof exists that this will always occur.
- The Analytical Approach: Initially, the characteristics of other players are unknown or incomplete, and so part of the approach involves developing a system of beliefs about the population, perhaps by using the Bandit or Exploration Strategies, (described below). If the statistical distribution of opposing strategies can be determined (e.g. 50% Tit for Tat, 50% Always Cooperate) an optimal counter-strategy can be derived analytically. The particular strategy that is settled upon is largely dependent on the characteristic and beliefs of the individual, whereas the preferred strategies of the larger population matter less. This is the “Single Tailored” option described in NovaSeeker’s post. Men who hunt for a better church, or go abroad in search of a wife, are seeking to place themselves within a social context having a different (less known) or unknown statistical distribution of opposing strategies. This offers a different set of boundary conditions in which a preferred solution can evolve through a Bayesian Nash Equilibrium. We could also call this the “Pioneer” or “Greener Pastures” approach.
The Bandit Strategy
Concerning new players (viz. players that have just been introduced as partners in an iterative game), the Bandit Problem models an agent that simultaneously attempts to acquire new knowledge (called “exploration”) and optimize his or her decisions based on existing knowledge (called “exploitation”). The agent attempts to balance these competing tasks in order to maximize his or her total value over the period of time considered.
The Bandit Problem is used as a model of resource investment to help determine the optimal dynamic allocation of resources to different projects, answering the question of how much time and money should be devoted to each one, given uncertainty about the difficulty and payoff of each possibility.
There are many practical applications of the bandit model, for example:
- Adaptive Routing (e.g. network development) – Focuses on broadening social connections and minimizing delays and inconveniences.
- Clinical Trials (e.g. medical research) – Investigating the effects of different experimental treatments while minimizing losses.
- Portfolio Design (e.g. stock finances) – Through trial and error, discovering and curating a tailored collection of personal traits and methods that will work best for developing a robust strategy.
We can see the Bandit Strategy being employed by people in the SMP. Men and women will opt to meet and date several partners, sometimes simultaneously, in order to “explore” and “exploit” (as described above), in an effort to quickly ascertain the return on investment potential, and which person(s) to pursue as a long(er) term interest.
The Exploration vs. Exploitation Tradeoff
In the practical examples described in the previous section, the problem requires balancing reward maximization based on the knowledge already acquired with attempting new actions to further increase knowledge. This is known as the Exploitation vs. Exploration Tradeoff in Reinforcement Learning.
To put forth an example, perhaps you may have noticed how those people who chose not to “play by the rules” are the same people who insist most vehemently on strict rule following.
What we are witnessing here is a manifestation of the Exploitation vs. Exploration Tradeoff in Reinforcement Learning. In simple terms, this means that people who have had a lot of experience and who have made a lot of mistakes, usually become wiser and more proficient in the process. These people are able to point out the problems and errors to others, but they cannot transmit their own wisdom of experience to others who have a different character and who face a different life situation. This is because the accumulation of knowledge can only happen through one’s own personal engagement in Exploitation and Exploration.
We also see the Exploitation vs. Exploration Tradeoff occurring in people who didn’t do much Exploration in their youth, and then suddenly realize later in life that they could have had a much better life, and could have attained more of their desires and goals, had they engaged in more Exploration. This sudden realization is called the Epiphany Stage for women, and Midlife Crisis for men. So then they try to overcompensate for regret by going heavy on Exploitation, (e.g. having an affair, getting a divorce, going back to the SMP, etc.), with the mind to use the knowledge they have gained through experience, and the resources and opportunities they have accumulated (which they were lacking when they were younger), to “revise their life choices” in order to actualize their own personality and realize their deeper desires in life.
Engaging in the Exploitation vs. Exploration Tradeoff is often colloquially called, “coming to terms with one’s self”. A quick, clean, easy process, it is anything but.
Interestingly, to those who never came to terms with themselves, it appears that those who insist on getting certain results in life by NOT following the same approach that got those same results for themselves, and instead insist on a different set of rules in which it is manifestly harder to obtain *any* results at all, leaving aside similar results to their own — well, it just seems asinine. They cannot relate.
It’s kinda like that aphorism, “Doing the same thing over and over, and expecting different results is a symptom of lunacy.” This impasse in communication is caused by a failure to engage in Exploitation and Exploration, and this hesitation is usually because one is afraid to forfeit anything in the Tradeoff.
Christians regularly scoff at the prospect of engaging in Exploitation and Exploration, but actually this is an important part of the maturing process, especially when we’re young. Jesus told us,
26 “If anyone comes to Me and does not hate his father and mother, wife and children, brothers and sisters, yes, and his own life also, he cannot be My disciple. 27 And whoever does not bear his cross and come after Me cannot be My disciple. 28 For which of you, intending to build a tower, does not sit down first and count the cost, whether he has enough to finish it— 29 lest, after he has laid the foundation, and is not able to finish, all who see it begin to mock him, 30 saying, ‘This man began to build and was not able to finish’? 31 Or what king, going to make war against another king, does not sit down first and consider whether he is able with ten thousand to meet him who comes against him with twenty thousand? 32 Or else, while the other is still a great way off, he sends a delegation and asks conditions of peace. 33 So likewise, whoever of you does not forsake all that he has cannot be My disciple.Luke 14:26-33 (NKJV)
One way to interpret verses 26-28 is that one must be willing to take great risks in his pursuit of Truth, and not care too much about the costs or the outcome. The Truth here is Jesus Christ specifically, but this also includes “coming to terms” with one’s relationship to Christ. Once the Truth has been obtained, then one is in a better position to assess his own strengths and position in life, and as described in verses 29-32, is less likely to make a catastrophically bad decision (or go through a mid-life crisis, etc.).
The Pavlov Strategy
In another strategy called the Pavlov Strategy (also called Win-Stay, Lose-Switch), the learning rule bases its decision only on the outcome of the previous play. If the play on the previous round resulted in a success, then the agent plays the same strategy on the next round. Alternatively, if the play resulted in a failure, the agent switches to another action.
For example, if the last round outcome was Defect/Defect, and then this is interpreted by the player as a failure to cooperate, then a Pavlov player will switch strategies the next turn. If for some reason, this same outcome is interpreted by the player as a success, then a Pavlov player will use the same strategy on the next round.
A large-scale empirical study of players of the game Rock, Paper, Scissors showed that some variation of the Pavlov Strategy is adopted by real-world players of the game, instead of the Nash Equilibrium strategy of choosing entirely at random between the three options.
For a certain range of parameters, the Pavlov Strategy beats all other strategies by giving preferential treatment to co-players who resemble the Pavlovian player.
The Tit for Tat Strategy
According to Axelrod, the winning deterministic strategy was Tit for Tat. This strategy is simply to cooperate on the first iteration of the game. After that, the player does what his or her opponent did on the previous move.
Depending on the situation, a slightly better strategy can be Tit for Tat with Forgiveness. When the opponent defects, on the next move, the player sometimes cooperates anyway, with a small probability (around 1–5%). This allows for occasional recovery from getting trapped in a cycle of Defections. The exact probability depends on the line-up of opponents.
This may partly explain why arranged marriages fare better over the long haul – because in an arranged marriage, both partners enter into the marriage with the mind to cooperate, even though they do not know what their partner will do. Also, they have the assumption that it is an iterative game, rather than a single iteration which is the common mindset among those who meet by chance and embark in whirlwind dating. As a consequence, the iterative game begins with a fast start having good returns, setting both partners farther along the learning curve.
The Cooperate-Defect Fiasco
Although Tit for Tat is considered to be the most robust (and most popular) basic strategy, a team from Southampton University in England (led by Professor Nicholas Jennings and consisting of Rajdeep Dash, Sarvapali Ramchurn, Alex Rogers, and Perukrishnen Vytelingum) introduced a new strategy at the 20th Anniversary Iterated Prisoners’ Dilemma Competition, which proved to be more successful than Tit for Tat. This strategy relied on collusion between programs to achieve the highest number of points for a single program. The university submitted 60 programs to the competition, which were designed to recognize each other through a series of five to ten moves at the start. Once this recognition was made, one program would always Cooperate and the other would always Defect, assuring the maximum number of points for the Defector. If the program realized that it was playing a non-Southampton player, it would continuously Defect in an attempt to minimize the score of the competing program. As a result, this strategy ended up taking the top three positions in the competition, as well as a number of positions towards the bottom.
This strategy takes advantage of the fact that multiple entries were allowed in this particular competition and that the performance of a team was measured by that of the highest-scoring player (meaning that the use of self-sacrificing players was a form of minmaxing). In a competition where one has control of only a single player, Tit for Tat is certainly a better strategy.
This strategy also relies on circumventing the rules about the Prisoners’ Dilemma in that there is no communication allowed between the two players, which the Southampton programs arguably did with their opening “ten move dance” to recognize one another; this only reinforces just how valuable communication can be in shifting the balance of the game.
The Cooperate-Defect Fiasco is employed in relationships, and much too often. One partner Cooperates more often than not, while the other habitually Defects. Also, the Defecting partner is frequently engaged in multiple socio-sexual interactions (or has had in the past), which allows the benefits of the Bandit Strategy to increase one’s learning, leading to greater confidence and capability. Adopting this strategy allows the Defecting partner to continually “win”, and this is usually manifested in his/her total control over the relationship. Meanwhile, the Cooperating partner sacrifices and suffers, sometimes indefinitely and without hope. Those who continually choose to be Cooperating within a Cooperate-Defect arrangement must have a strong and steadfast psychological justification for doing so, such as a stubborn Blue Pill mentality, self-righteous Chivalry, natural loyalty, the “Nice Guy” (or nice girl) syndrome, strong social reinforcement, religious beliefs, emotional codependency, “Hole in the Soul”, or a personality defect. While this is arguably the best strategy of all for the Defector, it is the absolute worst strategy of all for the Cooperator.
In addition to the fine arguments presented by Robert Glover in his book, No More Mr. Nice Guy (which I highly recommend), this is further evidence of why “nice guys finish last”. In other words, the Always Cooperate strategy is doomed to fail in a population in which any other self-interested strategy is practiced by an opponent.
Necessary Conditions for a Cooperative Winning Strategy
By analyzing the top-scoring strategies, Axelrod stated four conditions necessary for a strategy to be successful.
- Nice — The most important condition is that the strategy must be “nice”, that is, it will not defect before its opponent does (this is sometimes referred to as an “optimistic” algorithm). Almost all of the top-scoring strategies were nice; therefore, a purely selfish strategy will not “cheat” on its opponent, for purely self-interested reasons first.
- Retaliating — However, Axelrod contended that the successful strategy must not be a blind optimist. It must sometimes retaliate. An example of a non-retaliating strategy is Always Cooperate. This is a very bad choice, as “nasty” strategists will ruthlessly exploit such players, resulting in a Cooperate-Defect Fiasco.
- Forgiving — Successful strategies must also be forgiving. Though players will retaliate, they will once again fall back into a pattern of cooperating if the opponent does not continue to defect. This stops long runs of revenge and counter-revenge, and thereby maximizes “points”.
- Non-Envious — The last quality is being non-envious, that is not striving to score more than the opponent. In essence, this requires switching one’s definition of “success” or “winning” from the first definition to the second, given at the beginning of this essay.
Being Nice, Forgiving, and Non-Envious agrees with the general Christian concept of maintaining a healthy relationship. Of note, Eggerich teaches that both partners in a union must carry a good will towards the other. But one necessary condition for having a successful strategy, Retaliating, is consistently ignored or condemned by Christian circles as being “unchristian”, especially where men are concerned. Thus, it is of supreme importance to investigate and employ strategies that embrace conflict.
In addition, the Analytical Approach is often condemned as lonerish or anti-social, if not ignored altogether, and the Bandit Strategy is usually labeled as immature, even though it increases the learning rate, and improves one’s confidence and game playing capabilities.
Perhaps worst of all, the Cooperate-Defect Fiasco is often advised or pressed upon good-willed persons who are stuck with an evil-willed partner, with the idea that he/she is “suffering for (or like) Christ unto salvation”.
In our attempts to condemn and contain sin, we must be careful not to condemn those approaches and strategies which are most valuable and effective towards defeating sin.
So what’s your preferred table for playing ’52 Pick-Up? Cadillac styling, Chevy reliability, Dodge the whole thing, cannot afFord, GMC tough, Hornet stealth, Mack Truck’s bulldog indomitability, sexy Speedwagon, or will you go International?
Or to put it another way, as the bards of old have foretold,
“If you’re tired of the same old story, then roll with the changes!”
- Σ Frame: Conflict Structure and Marital Satisfaction (2017 November 15)
- Σ Frame: Book Review: The Love a Wife Desires, the Respect a Husband Needs (2017 December 18)
- Σ Frame: A Response to Stephanie’s Comments (2017 December 19)
- Σ Frame: The Pygmalion Project vs. Shared Enterprises (2018 February 2)
- Σ Frame: Looking at the Essentials (2020 June 12)
- Σ Frame: Some like it Hot (2020 July 3)