4.5 More on strategy and tactic

4.5.1 Creation of a tactic

4.5.2 Overlaying objects

4.5.3 Design bearers: genetic and non-genetic

4.5.4 Characteristic tactics of behaviour

4.5.4.5 Impact of memetic designs

The impact of memetic designs on our lives is huge, as shown by the case of Alois, a four year old boy taken by the Nazists from Poland and transported to Germany. Documents were prepared to show that his mother died in childbirth and his father was an SS officer, shot dead by Poles. He was no longer a Polish boy named Alois, but a German boy named Alfred Binderberger. Alois did not remember anything from his life in Poland and [...] grew up as a typical child in Nazi Germany.
[...]
When he was seven years old, the war ended: I am very worried that Germany lost. Hitler was my idol [...] in all the windows white flags appeared, and I roared. [60, p. 186].
After the war, his birth mother did everything to find her son. When Alfred was twelve years old, his new German parents showed him her letter. I said, "Dad, do not be silly, mom is sitting there."
"No, it's from your real mother."
"Come on!"
"From Poland" - he said.
And then I became angry. "Am I Polish! [...] I am definitely not a Pole!"[60, p. 187].

Finally, when he was fourteen, Alois met his real mother:
"He looked like a real Hitlerjugend" - she said - "a typical German".
[...] Alois did not know how to talk to her: "At first I was always anti, always against. The worst thing was that I did not know what to call her. I tried in the third person, very official. I avoided the familiar 'you' and never once said, 'Mom'."
Alois graduated from high school in Poland and entered the University of Warsaw. But he never fully accepted his native country and longed for his family in Germany [...] [60, p. 187].

In these dramatic circumstances, Alois also saw a genetic impact on his behaviour: I am the best example that we can love and understand each other. A Polish child that could be loved by German parents. I put it this way; in Poland, I felt that once again I had found my mother from Germany. It was the same - the same sensitivity. [...] The same love, the same devotion to her son. [60, p. 187].

Alois’ history makes it clear what impact memetic designs have on our lives - how strongly we are influenced by parents, teachers, colleagues, journalists, ideologists, books' writers, radio, TV ... – they are able to mold us as they want! And once we are shaped in our childhood it stays permanent.

4.5.5 Dependence of strategy in a society

In this chapter, I wish to honour the memory of John Maynard Smith(1920-2004), a British geneticist and evolutionist, who introduced mathematical game theory to the studies of evolution and the behaviour of living organisms. It is worth mentioning that he was not only a theoretician - during the Second World War, he was an aircraft designer. Richard Dawkins (b. 1941) played a significant role in highlighting John Maynard Smith's theories. He was able to change the dry and boring scientific language into a colorful and dynamic one, which made it easy to understand and memorable.

4.5.5.1 “Small evolution” – evolutionary-social game

This a variation on the classic "Hawk-Dove" game, but due to the significant rules change it deserves to be recognised in its own right - "Small evolution". The game is imposed on a population composed on a definde number of players (n). For a period of time all the players are randomly matched in pairs and play against each other in a contest called a single-game meeting. Depending on the strategies used, each player gains or loses points. After each single-game meeting, each player adjusts his account, wins and losses, and prepares for the next meeting after the pairs are redrawn. The most important changes in this game are the rules of selection and duplication. Occasionally, sometimes after the third round, sometimes after a million (nobody knows when), the selection occurs. When it happens, only the top 50% are selected for the subsequent cycle. The bottom half is rejected and no longer take part in the game - you can say that they will be annihilated. Those who pass the selection are cloned and gameplay restarts from the beginning. As you might notice, after this operation, the population remains at the same level of output.

The criterion for selection is the amount of points earned, so every point has value towards next cycle. John Maynard Smith called these points "points of survival". In our game, because they are the ticket to survival and multiplication, they are known as "points of life". It is worth noting that our game operates in a similar way to biological evolution - the winners take all.

Let's assume that there are two types of players in the population: workers and thieves. These titles, as in Smith's "Doves-Hawk" game show the strategy used by players in a single-game meeting. The thief is doing everything possible to steal the points from the opponent. Workers, when meeting other workers, generate resources. Below, the PayOff Matrix shows who earns and loses points depending on who they meet in a single-game meeting. For expample when a thief meets a worker, the thief earns 50 points whilst the worker loses nothing. The values in this matrix come directly from Maynard Smith.

"Small evolution" - PayOff Matrix "Classic Maynard Smith"
	Thief	Worker
Thief	T loses 25 (-25) T loses 25 (-25)	T gets 50 (+50) W no lose (0)
Worker	W no lose (0) T gets 50 (+50)	W gets 15 (+15) W gets 15 (+15)

The values, of course, can be considered as the parameters of the game. They play an important role so, in a while, we will discuss them thoroughly. But for now let's leave them as they are. By analysing this game with this payoff matrix, we can see which strategy is better. "Better" means which strategy gives a better chance to pass the selection process and, therefore, to multiply. Remember that the adjective "better" corresponds directly to this payoff matrix and this game. Let's start our analysis of a game with only one thief. He always collects 50 points in every single-game meeting, ensuring that he progresses to the next cycle and is cloned. Being the only thief guarantees his passage to the next cycle. After that occurs, the population now contains two thieves - the original and his clone, the rest are workers. We can see the tendency of the minority to increase. If the situation is reversed, there is only one worker in a population of thieves. The worker will not lose points in any single-game meeting, but the thieves will lose points any time they meet another thief. So the worker will pass the selection due to not losing points and get cloned. Therefore, we observe a tendency to increase the number of workers.

If there is one thief in the population, we notice a subsequent increase in the number of thieves. If there is one worker in the population, we notice a subsequent increase in the number of workers. Well, at some point there must be a state of equilibrium between the number of workers and thieves. It can be calculated as follows:

4.5.5.2 Disturbance analysis in “Small evolution”

4.5.5.3

4.5.5.4

4.5.5.5

4.5.5.6

A man is built of an unimaginably large number of cells that cooperate with each other in the most perfect way. Can this cooperation be explained in some way? Let's try. Imagine that a group of players playing among themselves in "Little Evolution" is - and here, pay special attention please - an individual player in the game of the next level above - in the next, overriding, "Little Evolution". Because we are creating a new type of object - a group of individual players into a single unit - and we want to distinguish it from other types of players, we have to name it somehow. Let us name the group of cooperating players, who are involved as a single player in a greater game, g-player. "Why do we not call them a team?", someone will ask. Because the players in a team go home after the match, while our g-players take part in "Little evolution" all the time. Because of this they are submitted, as a g-player, to selection and duplication. This game within a game is another, more complex, evolutionary game that we will name "Little Group Evolution".

To better understand what is going on, you can imagine it as a specific hockey tournament, in which four teams take part: White, Reds, Blues and Greens. After playing six matches - each playing against each other - a ranking list is created. In the case of both teams having equal points, the positions are determined by a coin toss. The two bottom teams from the list are eliminated from the tournament, while the two top teams are cloned. Almost perfect copies of each player from both teams are created. If, for example, in the first round the winners were the Reds and the Greens, then the R1, R2, G1 and G2 teams would participate in the following tournament. Again, each team (g-player) would play against each and, once more, selection and replication would take place. If this time the selection favoured R2 and G1, the g-players in the next round would be: R2a, R2b, G1a and G1b. And so on.

Na tym etapie rozważań przyjmijmy, że reguły nowej gry prawie nie różnią się od reguł „Małej ewolucji”, z jednym wszak upraszczającym sprawę zastrzeżeniem: „spotkania – gry” pomiędzy g-graczami nie są istotne, a to, który z nich zostanie sklonowany, zależy wyłącznie od tego, ile każdy z nich jako g-gracz potrafi wewnątrz siebie wypracować punktów przetrwania. Dlaczego nie chcemy, by g-gracze grali w „Małą ewolucję”? Po prostu dlatego, by nie komplikować. Ponieważ jest to pierwszy poziom analizy, już samo porównanie stanu kont punktów przetrwania u poszczególnych g-graczy w zupełności wystarczy. Co do klonowania, to w „Małej ewolucji grupowej” polega ono na tym, że powielani są wszyscy wchodzący w skład g-gracza – tworzona jest wierna kopia każdego gracza z g-gracza. Jako funkcję selekcyjną przyjmijmy najprostszą, taką, która przepuszcza tylko tych z górnej połówki listy, czyli najbogatszych w punkty przetrwania. Zbadajmy teraz, jak zaburzenia taktyki pojedynczego gracza będą wpływały na los g-gracza w grze nadrzędnej. Jako tabeli wypłat użyjmy macierzy „Jak to w życiu” i sprawdźmy, co się stanie, gdy taktyka jakiegoś gracza w g-graczu ulegnie mutacji, a mutant złodziej będzie lepiej okradał pracusiów? Już to przerabialiśmy: w tym g-graczu złodzieje wyprą pracusiów, niszcząc Ryc. 86. Gra w hokeja. Dwie drużyny walczą ze sobą, ale zawodnicy należący do tej samej drużyny świetnie ze sobą współpracują [Wikipedia ang., stan 2009.10.12, na podst. OHL-Hockey-Plymouth-Whalers-vsSaginaw-Spirit.jpg - public domain]. 269 4. Czym jest życie? tym samym źródło punktów przeżycia. Wniosek jest jeden: jakiekolwiek zaburzenie prowadzące do obniżenia wydajności źródła punktów g-gracza powoduje, że zmniejsza się gromadzona przez niego całkowita liczbę punktów, a to jak wiemy, skutkuje wyeliminowaniem go z kolejnej rundy gry nadrzędnej – „Małej ewolucji grupowej”. Widać zatem, że mamy do czynienia ze zjawiskiem zapadki ewolucyjnej, która przepuszcza tylko te zaburzenia, które poprawiają efektywność źródła lub osłabiają efektywność anihilatora! Żadne inne zaburzenie nie przechodzi przez sito selekcji i nie jest klonowane! Analiza tej gry wyjaśnia, w jaki sposób mechanizm zapadkowy przyczynia się do wytwarzania i udoskonalania taktyk współpracy pomiędzy graczami wchodzącymi w skład g-gracza!