Divisão de Observação da Terra e Geoinformática

Predictions are not the pinnacle of science. They are useful, especially for falsifying theories. However, predicting cannot be a model's only purpose. […] But surely the insights offered by a model are at least as important as its predictions: they help in understanding things by playing with them, just like a child learns much by playing with dolls (p. 4)

What is essential is to take the step into abstraction (p. 5)

“Evolution is tinkering” François Jacob (p. 5)

“We may find illustrations of the highest doctrines of science in games” (p. 7)

This chapter discusses the differences and similarities between mathematics and biology. Mathematics is “broader” but unconnected to the real world. Biology is the opposite. He also exemplifies the difficulty to make mathematical models in biology.

History of the game of life.

Cellular automaton: each cell can be at any given time in one of several possible states. The transitions between states from one time-step to the next depend on the states of the cell and its neighbours. They are determined by well-specific rules - the same rules for all cells and all time.

The point of Live is to show that even if the physics were to succeed [in finding a model which rules the universe], the world could nevertheless remains as darkly mysterious as before.

The [self-replicating] program has to be used in two ways: it gets translated and copied. In one role, it is in command, causing a sequence of activities; it manipulates the machine. In the other, it constitutes passive object of the copying unit. (p. 19)

von Neumann implemented a self-reproductive cellular automaton. Conway made a very simpler one (simpler in the sense that it uses only two states - black and white)

Life, in this sense, is unpredictable, in spite of its simple transition rules: unpredictable not just because of the deficient wetware which we carry in our skulls, but fundamentally (p. 37)

Lotka-Volterra equations for predator-prey studies.

In order to exclude interactions with other populations, ecologists have followed the fortunes of laboratory populations (for instance, of fliers kept in jars) […] the population […] keep cycling, driven not by predation but by intrinsic dynamics. (p. 48)

“Is there always an advantage in replacing a blurred image with a sharp-focused picture? Isn't the blurring frequently just what one needs?” Wittgenstein

In the abscence [of the feedback's time-delay], an isolated population settles down tamely to a steady state delimited by the environment's carrying capacity. This is simply due to competition within the population. Every supplementary member impedes further growth (p. 56)

There cannot be more species than resources around. G.F. Gause, competitive exclusion principle (p. 57)

Ellsberg Paradox: people prefer games with complete information even when the game with incomplete information offers the same chances. (p. 77)

The probability for a mutation to occur does not depend on whether this mutation is advantageous or not, whereas the probability for the mutation to spread does so depend.

Monod's book: “chance and necessity”

The population produces many more offspring than their environment can sustain - limitation of living space.

Kimura: surprisingly many molecules exist in several variants within one population. Some individuals carry one variant, some another. the neutral theory: when one compares the genomes of existing species, the vast majority of molecular differences are selectively “neutral.” That is, the molecular changes represented by these differences do not influence the fitness of the individual organism.

Under random drift, neutral mutants may arise in a population. (I saw it somewhere in Nowak's book)

How traits are passed to the next generations. Mutation as an error of copy. Crossing-over.

[…] By judiciously mixing their strategies, players can always maximize their minimal payoff, or, what amounts to the same, they can minimize their opponent's maximal payoff. This holds for all zero-sum games, where the gain of one player is the other player's loss. […] There is a whiff of pessimism behind such minimax thinking in terms of security levels. By assuming that the coplayer is going to find the most hurtful reply, minimax evaluates strategies according to their worst possible outcome. […] Above all, it credits the adversary with an at least equal amount of intelligence. Countless defeats are due to downplaying the adversary's threat. However, the minimax philosophy works only if the other player is really out to get out. If your win is the other player's loss, this is a reasonable assumption. But there are many situations where the interests are not totally opposed. Even in war, the two parties are likely both to wish to avoid certain outcomes. […] Take the 'Chicken', for instance. While there is plenty of antagonism in this game, the two drivers are not entirely at odds: both agree on not wishing to crash. Where they disagree is on who should do the avoiding. […] How would you play this game? Your worst outcome - the crash - is worst for your adversary too. By swerving, you maximize your minimal payoff. By not swerving, you minimize the opponent's payoff - no longer the same thing. You are in the same position as your adversary, but you should try to do whatever the other does not. Most game theorists agree on 90 per cent as the 'rational' solution, but the argument is somewhat tenuous. (p. 163-164)

We have seen how difficult is to justify why a single such encounter, a player should swerve with 90 per cent probability. But the two players are now supposed to be members of a large population and meet randomly. It makes sense, here, to assume that its members 'swerve' with a probability of 90 per cent. […] Within a population, self-regulation would adjust the average probability of escalation at 10 per cent, because the players now experience the average level of escalation, react to it, and thereby affect it. If most members of a large population adopts it, then no mutant strategy can invade the population.