Può l'altruismo emergere dall'evoluzione genetica o siamo condannati al predominio spietato del 'gene egoista' di Richard Dawkins ?
Le pagine che seguono danno una risposta a questa domanda mostrando come l'emergenza della cooperazione e dell'altruismo siano una congettura tutt'altro che peregrina.
La lettura dell'articolo richiede un certo impegno, che verrà premiato dalla comprensione di un tema scientifico di grande rilevanza.
SELFISH GENE OR ALTRUISTIC ORGANISMS ?
Guido Tascini
Università Politecnica
delle Marche, via Brecce Bianche 6
60131 Ancona, Italy
g.tascini@univpm.it
The paper presents an overview of works on emergence in genetic evolution of altruism and does some
hypotheses, discussing the simulation approach to the evolution. After an
analysis of various works, including approaches to Biology, Social Science and Simulation,
some assumptions are made about the role of information and about some exceptional results of Game Theory. To confirm the
hypothesis discussed analysis results of past simulations are reported in the appendix. It seems to emerge plausibility of cooperation, and
then of altruism: contrary to the claims
made by Dawkins and by other evolutionists. The question remains: this is just a simulation or the results, particularly
those of Axelrod, and the theory of Trivers lead us to rethink what really is self-interest?
1 Evolution
From biological point of view evolution
is the phenomenon of changing, through successive generations, the genetic
heritage of species (genotype) and consequently its somatic manifestation
(phenotype). It is a process that is based on
the transmission of the genetic heritage of an individual to its progeny, and
on the interference in it
interposed by random mutations.
Although the changes between one generation and the
next are generally small, their accumulation over time can result in a
substantial change in the population: This occurs through the phenomenon of
natural selection and genetic drift, until the emergence of new species. The
morphological and biochemical similarities between different species and
paleontological evidence suggests that all organisms are derived through a
process of divergence from common ancestral progenitor.The theory of evolution of species is a cornerstone of modern biology. In its essentials, is due to the work of Charles Darwin, that considered natural selection the main driver for the evolution of life on Earth. Key findings were the laws of Mendelian[1 ] inheritance of characters and then the discovery of DNA[ 2] and genetic variability[3 ].
Although the general principles of evolutionary theory are consolidated in the scientific community, there are secondary aspects of the theory that are still widely debated, and are a very exciting field of research. The concept of evolution has been a revolution in scientific thought of Biology, yet inspired numerous theories and models in other fields of knowledge.
Evolution is not a process of improving the species: mutation and selection produce adaptation to the habitat. So it can also lead to loss of character and functions. The habitat is the set of environmental conditions and relationships with other species subsisting at any given time. And it is both a source of selection and land of adaptation: too rapid a change in environmental conditions may also cause the extinction of populations highly specialized.
2 Games, insects and cooperation
Richard Dawkins in The Selfish Gene[4] wanted to discuss the biology of
selfishness and altruism, and then he
reinterpreted the basis of evolution, and therefore of altruism. But he did
not want to lay the foundations of a morality based on evolution, and we do not believe that altruism is part
of biological nature. But John Maynard Smith[5] has shown that behaviour is subject to evolution. Robert Trivers[6] showed that reciprocal altruism is strongly
favored by natural selection and that, based on the subject of Kroptokin[7] we
can see cooperation as a factor of evolution, rather than competition. But
Axelrod[8] was shown, with an easy game,
that conditions for survival, such as "Be good, be provocative,
promote cooperation," seem to be the basis of morality.
Although this is not the basis for a moral science,
however, game theory has shown that Darwinian natural selection can lead to
complex behaviors, and may leave room for concepts like morality, kindness and
justice. From here you can see that self-interest has deeper implications than
previously thought.
We can also think to
extend the result to moral and social
contract, to solve the old problem that opposes the private interest to the
group.
The insects were a world to study, particularly exciting to
see how cooperation arises
For example, bees,
insects, where workers are genetically sterile and unable to transmit their
genes. And they is a gene that makes an individual aid other individuals who
have the same gene, also using the sacrifice [9].
Genetic relatedness is known to have played a major role in favoring the
evolution of cooperation in social insects and in the evolution of complex life
in general [10]. The biological theory of kin selection, formalized by JBS
Haldane [13]and W. D. Hamilton [9], explains how individuals can exhibit
strategies that favor the reproductive success of genetic relatives, even at a
cost to their own survival and/or reproduction.
Other insects suitable for the study of
cooperation are the ants, that compose
about 15% of the animal bio-mass in most terrestrial environments.
The reasons for the success of insects, like ants, termites, social
bees, for these studies are their ability to cooperate and to perform efficient
division of labor.
The high levels of
cooperation found in social insects have given rise to the idea that colonies
can be viewed as "super-organisms", operating as a single functional
unit. This is an approach to group
evolution. But this contrasts with the
claims of Dawkins which
says that the fundamental unit of selection. and then of selfish, is not the
case, neither the group nor in the strict sense the individual: is the gene, ie the unit of
heredity.
And the reasonableness of this evolutionary
approach, albeit in artificial systems, did not have easy acceptance among the
Orthodox Evolutionism and required a long series of studies and demonstrations.
Charles Darwin's theory
explains how evolution works , that
is "by means of Natural Selection and is
explicitly competitive, in that "survives only
the fittest", and
Malthusian,
that is emphasizes the "struggle
for existence". Species are opposed to species on shared resources, similar species with
similar needs and similar niches, and still more are opposed individuals within
species. All are all rivals, and compete in the production of offspring.. Darwin's
explanation of how preferential survival of the slightest benefits can lead to
advanced forms is the most important explanatory principle in biology, and
extremely powerful in many other fields. This reinforce the idea that life is a war of each against all, where
every individual has to look out for himself, and that your gain is my loss.
In
this philosophical context, altruism,
that is voluntarily yielding a benefit to a non-relative, and cooperation, which is working with
another for to achieve a common good, seem completely antithetical to the self. However, cooperation and altruism have evolved and
evolutionists can not explain why. Darwin
described how evolution works in simple
manner, but the implications in complex phenomena was taken over a century. The altruism[11] by definition reduces personal fitness, and
its emergence is explained by sociobiology [12]
Altruism
was provided by the theory of group
selection, that was suggested by Darwin for the social insects. This argues
a natural selection acting on groups. groups are more successful and l benefit the
individuals of the group, even if not related. But it was not fully persuasive;
mainly because of cheaters, that participate in the group without
contributing.[14]
Genetic kinship theory of
William D.
Hamilton[9] contributed to the altruism. A gene may
cause an individual helping other individuals having copies of that
gene: for this the gene has a net benefit,
including even the sacrifice of a few
individuals. For instance in the social insects, the workers are sterile, and
therefore they are incapable of transmitting their genes . These anyway benefit the queen, just passing copies of "their" genes. Further
elaboration was done in the
"selfish gene" theory of Richard Dawkins, for which
the unit of evolution is not the individual organism, but the gene. This
according with Wilson.that sais "the organism is only DNA's way of making more DNA.". Kinship selection works
when involved individuals are closely related; but what about the presence of altruism and
cooperation between unrelated individuals, across species?
Robert Trivers [16]
showed that reciprocal altruism can evolve between unrelated
individuals of different species.
The relationship of the involved individuals is analogous to some situations of the Prisoner's Dilemma[15 ]. The key is in the IPD (Iterated Prisoner's Dilemma) where both parties can benefit from the exchange of many altruistic acts. Specifically, altruism generates altruism. And the benefits of human altruism appear to come directly from the reciprocity, and not indirectly from non-altruistic group benefit. The Trivers' theory was crucial in the history of altruism. It replaced group selection, and predicts various observed behavior, like: gratitude and sympathy, moralistic aggression, guilt and reparative altruism, abilities to detect and discriminate against cheaters. Finally the emergenge of reciprocal altruism has been demonstrated in Tournament organized by Robert Axelrod[8] around 1980. In these tournaments, players are challenged with different strategies, for 200 iterations of the PD. Won the simple TFT (Tit for Tat) strategy, submitted by Anatol Rapoport: first move cooperate, the next reciprocates what the other player did on the previous move.
The relationship of the involved individuals is analogous to some situations of the Prisoner's Dilemma[15 ]. The key is in the IPD (Iterated Prisoner's Dilemma) where both parties can benefit from the exchange of many altruistic acts. Specifically, altruism generates altruism. And the benefits of human altruism appear to come directly from the reciprocity, and not indirectly from non-altruistic group benefit. The Trivers' theory was crucial in the history of altruism. It replaced group selection, and predicts various observed behavior, like: gratitude and sympathy, moralistic aggression, guilt and reparative altruism, abilities to detect and discriminate against cheaters. Finally the emergenge of reciprocal altruism has been demonstrated in Tournament organized by Robert Axelrod[8] around 1980. In these tournaments, players are challenged with different strategies, for 200 iterations of the PD. Won the simple TFT (Tit for Tat) strategy, submitted by Anatol Rapoport: first move cooperate, the next reciprocates what the other player did on the previous move.
How to study
phenomena that involve great time scale, like the living being evolution? Computer Science gives us the opportunity of
simulating by computer processes and arranging virtual experiments: these
imitate natural evolution, and allow investigating processes that, in nature,
take place over millions of years. In this case clearly defining the basic laws
of transformations and/or evolutions of phenomena to study, appears
fundamental. A well-established theory
that gathers today the near total biologists is the current dominant
explanation of Darwinism, the S.T.E. [17,18]. This theory is actually the base
of research on genetic transformation of human beings, but it is also the
reference for some studies on psyche sphere and on social evolution. There are
two fundamental elements of the theory:
1) random genetic mutations, 2) sorting, by the natural selection, among
those which are favourable to gene or the species (Dawkins 1990). The natural
selection and adaptation involves the
phenotype, that is the inventory
of inherited tracts, and is an adaptation to demand of ecological situation.
In practice adaptation stands for process in which the environmental variables
select, among individuals in a population, those whose inheritable properties
are the best-suited for survival and reproduction. This theory, that uses a random selection,
constitutes the Hard Darwinism, and has received critiques [19, 20, 21,22], the
most centred on its substantial finalism. The genes, the individuals or the
species most suited for survival, depend on the favourable variations in
natural selection. But even if it is a random genetic variation result, the
mechanism may be considered a utilitarian and finalist design. Modifications
are proposed, but the situation is still evolving. Therefore we are wondering
if the scientific community is going toward a Week Evolution Theory. The Darwinism was recently integrated
by several contributions: molecular biology [ 23] molecular genetics;
population genetics [27]; punctuated balances [24]; neutral theory [28];
genetic drifts; etc..The criticisms addressed to the theory may be summarized
in the following observation: if it can account for the microevolution, either
by phyletic gradualism, or by punctuated balances, it does not explain the
macro one and the mega evolution. In practice the independence, outlined by the
theory, between the genome and the cytoplasm are not guaranteed. In fact the
cellular core permanently interacts with the cytoplasm. In the cell they are permanent exchanges of matter, energy and
information, like it is shown by the
Molecular and cellular biology ; in these exchanges take part all the
cellular organoids, nuclear and cytoplasmatic: chromatin, mitochondries,
nucleoles, Golgi apparatus, etc.. Moreover the fundamental concept of strictly
random mutations is negated by several molecular genetics experiment and
observations. For instance the colon bacilli may have an abnormally high
mutations able to metabolize lactose in a stock unable to be nourished (Cairns,
Overbaugh, Miller 1988); similar experiment
is realised on the bacteria Escherichia coli with respect to salicin
(Barry Hall, Rochester); mitochondrial D.N.A. and mitochondrial mutations
existence was observed, for which it is hypothesized interactions between
D.N.A. mitochondrial and nuclear D.N.A. at the mitosis final stage (telophase)
[58 ]the transcriptase opposite
transforms the R.N.A. of certain viruses in D.N.A.; etc.. There are
some suggestion to introduce probability in the interaction [56, 57] between
the environmental evolution and the evolution of the organisms. The environment
parameters may be various: chemical stimuli, like C, N, H, P, S, etc.; physical
stimuli, like electromagnetic waves, sound and vibrations, temperatures,
pheromones, etc.; ecological stimuli; pray-pray relations in the beasts; etc...
The organism’s reaction to the
environmental factors influence is complex, being the biosphere very complex,
and they are located at the genome level,
as well as at levels of molecular biology, embryogenesis and anatomical
structures. The relation between
environment parameters and organisms is of probabilistic type and integrates
collective phenomena affecting simultaneously distant classes and phyla
(Invertebrates and Vertebrates). From
this analysis the basic theory that may be hypothesised
for simulation model is a Evolution Theory, in which an organism
interacts with the environment in a complex way , and reacts to many stimuli of various nature: actually
known and still to analyse or to discover. Now it is clear that the environment
and its interaction with organisms is the key of the theory. The interaction
may be probabilistic and the selection is not so blind, but depends on
environment conditioning; and the environment conditioning and finalization is
still largely to investigate.
A set of individuals may be viewed as complex system and then we can
take care of emergences.
Many individuals that evolve may give rise to unpredictable behaviours, that we
call emergent. And it is clear
that the emergence postulates an observer that sees the emergent
behaviour visible at a higher level. In our case we have to hypothesize a level
higher then genetic one were the mutations happen. Then if we hypothesize a
stratification of the formal theory levels we can localize this emergence
observation at a meta-genetic level.
In a “virtual” context we conceived a model with fitness function that drive the
adaptation to the environment and that take care of interactions previous defined.
In our experiments we will also hypothesize that the fitness function will take
care of an environment feed-back
on the list of individual to select for the survival. A computer simulation may speed up the
evolution process if the goals change continuously [59]Computer simulations
that mimic natural evolution, allow to investigate processes that, in nature,
take place over millions of years. We can simulate a population of digital
genomes that evolves over time towards a given goal: to maximize fitness under
certain conditions. Like living organisms, genomes that are better adapted to
their environment may survive to the next generation or reproduce more
prolifically. For instance the work of
Nadav Kashtan, Elad Noor, and Uri Alon suggests that varying environments
might significantly contribute to the speed of natural and/or artificial
evolution. Although the computer
simulation is useful for studying theoretical questions of evolution, it may
have some practical implications in engineering fields for systems design, and
in computer science, for accelerating the optimization algorithms.
We done
experiments on artificial evolution. [60] The computational model adopted in our experiments is
inspired to Holland Model, including a feed-back of the environment on the
individual choice. The simulation plans N strings that are random generated. Each
string (genotype) is the binary code of a candidate solution (phenotype). At
each genotype gi of initial population Pop(t=0 at time
t=0, is associated a value of the fitness function ƒ i= ƒ (gi),
that represents the ability of the individual to adapt itself to the
environment. For detecting the adaptation value the fitness function receives
in input a genotype, decodes it in the corresponding phenotype and checks it on
the environment. After completion of the evaluation phase of individuals of the
population Pop (t), at the time t, a new population Pop (t+1),
at the time t+1, of N new candidate solutions is generated; this standard algorithm
evolves neural network and structural
model of RNA. The population of N individuals is initialized to random
binary genomes of length B bits (random
nucleotide sequences of length bases). They are several generations: in each generation, the S individuals
with highest fitness (the elite) remain unchanged for the next generation. The
individuals with least fit are replaced by a new copy of the elite individuals.
As the non-elite individuals, pairs of genomes are recombined (with crossover
probability Pc), and each genome is randomly mutated (with probability Pm
per genome). A simulation runs until
max. of fitness function ƒ i is achived for the goal.
How to simulate a fair-unselfish
model ? In general, systems that replicate need resources
(energy, space,) for building copies of them. Resources are normally limited
and, since each replicator tries to produce a maximum of copies, it will
attempt to use resources to the limit. Then when several replicators are using
the same resources, there will be competition or conflict. The more efficient
replicator will gradually succeed in using more and more of the resources, and
the less efficient one will succumb. In the long term, nothing will be leaved
for the less fit one, and only the fittest will survive. The concept of
‘altruism’ is present in literature [
36], and means that a system
performs actions for increasing the fitness of another system using the same
resources. On the other side selfishness characterizes a system performing actions that increase its own
fitness.
5 The evolution of human
society
The
human social evolution is considered to fall within the Evolution. It is
worth emphasizing, with biologists, like the traditional concept of
"organism", such as separate living entity, is considered usable only
in limited domains. There are many examples,
such as viruses, hives, mold and other parasitic and symbiotic relations that
tend to blur the distinction between living organisms and communities as a
elementary living system. However, even
if we assume the existence of separate bodies, scholars recognize that the
hierarchy of evolution is marked by neural metasystem transitions (MTS), or
jump in the evolutionary continuity between the organisms. And simultaneously
occur metasystem transitions which lead to cluster organisms; elementary examples are: the reproduction of
populations, the dynamics of colonies of the fish and flocks
of birds. The cybernetic vision asserts that when the control of these groups
is very strong there are more marked transitions (eg develop multicellular
organisms). While when the control group is
weak appear societies of organisms. The integrated form of the society can be
found in social insects: ants, bees and termites. But
moving to human society, everyone recognizes that this is much less tightly
integrated and more complex of insect
societies. Societies are characterized by higher culture, which is transmitted
from one group to another with information models in a way not genetic. There
is a theory that defines this information, not genetic, and transmitted between
people, which introduces the concept of the memo. Memes
are information structures, similar to genes that may undergo changes that
resemble changes and selections of evolutionary type: they are characterized by
mutations and recombinations of ideas, as well as by their distribution and
reproduction or selective arrest. This theory provides a social evolution of human
cognitive thinking, that becomes ability
to control production, reproduction and association of memes in the minds of
men. Hence the possibility of evolution
of memes. According to this theory the human
thought is a systemic ‘emergence’;
this emergence makes it appear a new mechanism of evolution: conscious human
effort rather than natural selection.
The variation and selection necessary to increase the complexity of the
organization now has in place in the
human brain, it becomes inseparable from the free acts of human beings. According to this view, the emergence of
human intelligence, and memetic
evolution, rise further metasystem
transition that represents the integration of people in human society. Human
society is qualitatively different from that of animals, and includes among
distinctive features the ability to
create language, which on the one hand enables individuals to communicate with
each other, and allows men to create models of reality the other side. The
theory addresses the hierarchy of groups: the levels are grouped and organized
in terms of work. And at each level
there is a problem of metasystem transition (MTS). At
each level, there is not only competing interest groups at the same level, but
also between smaller units and larger ones. Groups vis-a-vis, are incorporated
into organized city-states and city-states into nations. Each of these levels
are the places where they occur, the selection and competition. From the perspective of Cybernetic many evolutionary
biologists dispute the effectiveness of biological group selection, which is
altruistic. These are individuals with
'altruistic' behavior: that
individuals act for the preservation of the group, risking their own survival
and the chance to qualify for the "inclusive fitness" (representation
of their genes in future generations).
They argue that altruistic diminish their chances by paying a price for
the risk they run and are therefore disadvantaged in the competition between
genetic groups. But
others think, as we have seen before, that any social change occurs through
conscious human effort. This may open a breach in the selection process, that eventually no longer be blind. Other works about altruism in evolutionary context are in [43,44, 45, 46, 47, 48, 49, 50, 51].
To simulate in a virtual
environment the evolution of species, are interesting also critical voices of
neo-Darwinism. Let us consider two extreme, criticizing, natural selection, and
placement of evolution selfish in catastrophic situations. Fodor [52,53], asserts that the orthodox neo-Darwinism
is using internally knowledge, which requires in what it want to explain. For
example, the correspondence between organs and functions that blind evolution
can not provide: the heart has been selected to pump blood. In
Darwin's theory must distinguish two parts: the phylogeny and natural selection
with adaptation. The phylogeny is acceptable, but the adaptation shows some
problems. In practice Darwninism is challenged by natural selection: natural
selection theory, leads to characterize the formation mechanism, not of species
but of all evolutionary changes in the innate properties of organisms. In
agreement with the theory of selection, a phenotype of a creature - an
inventory of its heritable traits, including its hereditary mental traits - is
an adaptation to the demands of its ecological situation. Adaptation is the
process by which environmental variables select among the creatures of the
population, the ones whose heritable properties are most suitable for survival
and reproduction. So environmental selection for adaptation is the process par excellence that runs through
the evolutionary tree. But without intentionality that is excluded
from selection, the phenotypes are selected for anything. So to say that
evolution has selected an organ for a given function corresponds to a statement
of intent. Insert the for means to introduce a purpose. And this
can expect to get with two types of solutions: first, by using a higher level of intentionality.
Mother nature, the selfish gene, or God the Creator have entered in life
tension. Second, introducing a 'law', which is also
intentional. The biophysic Voeikov [54, 55],
questions the evolution of biologists in its traditional form. According
to Darwin's theory all of nature including man, is the result of a natural
evolution. Through processes of a few billion years, microscopic organisms have
become more complex and finally man appeared.
This transformation, over time,
from simple to more complex forms instead of evolution, it would be better to
call development
process. Also Voeikov states that this theory contradicts the
everyday reality. Consider, for example, a complex living organism, which
arises from an egg cell. There are in it the various cell differentiation, but
the thing we wonder is how all cells work in synergy, one another, in
symbiosis-cooperative, as they say, and once each for herself. If these cells behave in
accordance with the neo-Darwinian theory should multiply in geometric
progression, according to random evolutionary processes that develop in the
absence of resources and lack of energy and substances. When these processes are set up in everyday life, we call cancer. The
tumor cells develop differently from those from which are derived by mutation
and does not resemble at all: are more
aggressive and more greedy of healthy cells, origin; feed consuming more energy
and more substance and less effective. This
model represents the exasperation of Darwin's theory. Another typical example, that of locusts
appear suddenly developed from another type of insect, living without
disturbing; consist of a limited number of individuals, subject to regulated
reproduction, which is kept in balance for a period time characteristic of the
type of insect. Then the imbalance. The
imbalance is not created because they have to eat, but is the result of
processes that do not yet know, it relates to climate and solar activity.
Suddenly he destroys the rule of multiplication. If this factor of disturbance lasts for 2-3
generations, insects from normal insects begin to multiply like a cancer, which
reach a large size, demonstrating that there is an abundance of power resources
and energy. Then show an anomalous behavior: the
herd that is generated begins
to move in a certain direction, not to search for food. Always goes in that direction, without ever
stopping or reversing direction. And if along the way meets plantations,
destroys and continues. Sooner or later
eventually reach the sea and commits
suicide.
6 Choices
for the simulation of altruistic behaviourHow cooperation and altruism can emerge during evolution [32]? Darwinian evolution is thought to occur through a blind variation and the natural selection. This includes biological, but also physical, chemical, psychological or socialprocesses.
Natural selection is survival, ie the selection of the individual, who is best able to adapt to the environment. Fitness corresponds in general to the probability of encountering the same or a similar system in the future. Systems that are stable (they tend to maintain for a long time) have a high fitness, and/or at their disappearance leave many offspring; that is they are systems that produce many other systems which are, in a sense, replicas of themselves. Such self-reproducing systems are called replicators [4,50,51].
Natural selection acts on systems which have insufficient fitness, that are unstable and do not produce offspring, by eliminating them, and the process occurs spontaneously and continuously.
Systems that replicate need resources (building blocks, energy, space, ...) in order to build copies of themselves. Since resources are limited a replicator tend to use them to the limit . If there are more then replicator, competition arises between them. If the competition continues, we come gradually to a situation where some replicator overwhelm the weak. And finally there remain only the most suitable.
Another approach to the unselfish approach is those of the memes. [33]. Meme evolution is much faster and more flexible than genetic evolution.. A fundamental difference between memetic and genetic fitness is related to the metasystem transition: The emergence of cooperative systems is connected in general as to a "metasystem transition", where interaction patterns between competing systems tend to develop into shared replicators, which tend to coordinate the actions of their vehicles into an integrated control system.
Definition of Metasystem Transition. [61] Let a system S from which to make some number of copies, possibly with variations. Suppose that these systems are united into a new system S', and constitute the subsystems of S’. S’ includes also an additional mechanism which controls the behavior and production of the S-subsystems. We call S' a meta-system with respect to S, and the creation of S' a metasystem transition. Consecutive metasystem transitions generates a multilevel structure of control, which allows complicated forms of behavior.
Another consideration is related to the evolution simulation. No universal set of building operators exist in Nature that which directly produces different patterns, such as proteins, proteins, or even more complex organisms. Nature solves this task indirectly, through evolution. [50].
A simulated evolutionary process essentially terminates when a best fit is found. Further evolution is impossible; perhaps we can introduce some tricks to change the selection criteria, interacting with the simulation system. But the substance does not change.
It appears that selection alone cannot produce sustained evolutionary growth.
A sustained evolution may be viewed as an iterative process, consisting of selection steps linked by causal steps. Selection steps may be realized by evolutionary algorithms. Causal steps involve implicit components, expressed as the “depth” of causation.
During evolution individuals can communicate with each other and the environment. In the absence of communication between individuals, the strategy of "selfish gene" prevails due to lack of information. In this case the group conveys little information and are ill-suited for their environment. Referring to the Prisoner's Dilemma transformed: the optimal strategy is one that adds the penalties of prisoners, and looks for the situation with the minimum of this sum; in other words, it supports precisely that strategy ( of the group) that Dawkins does not accept. The non-communication between the two prisoners can lead to an initial choice selfish and therefore dangerous if the other acts as selfish; However, Tit for Tat strategy of Anatol Rapoport in Robert Axelrod's tournaments, a fact proved decisive: as individuals acquire knowledge of each other, a situation of closure selfish, may gradually come to cooperation; it can strive for optimal strategy for the group, what in the PD game can not be taken for lack of agreements. This is indicative of the role of communication between individuals. The TFT strategy is in fact: first move cooperative, following the same of opponent last. That is, the player is knowing your opponent gradually and, of course, when knowledge is highest, you win: ie we reach the optimal situation for cooperation. As you see, to reflect well on the role of information, situations may arise evolutionary and unthinkable so far refused.
So a theory by which the individual-individual and group-environment interaction occurs through communication of information seems adequate. The idea is that: when individuals communicate, they know and then natural selection no longer occurs at the level of the gene, typical selfish closed in itself, but at group level; the group is the top level of knowledge and bonds and then Solidarity: by adopting anthropomorphic metaphor also at the level of genes and biological organisms. In these circumstances, the evolution, viewed in terms of information, goes to the maximum entropy of the group. In fact, the information entropy related to survival of individuals in the group can be written:
where pì is the probability of survival of i,th individual. In the absence of communication, the probability of survival of the selfish is gratest.
The random number that represents the information of the group is heavily unbalanced in probability and hence its average value, which is precisely the Information Entropy, is low. Assuming that individuals communicate among themselves weakens the competition between them. In this case the survival probability of individuals are almost equal and this, in terms of probability implies a maximum entropy. At this point it is no longer the individual to plunder the resources of environment, but the group that connects with this, exploiting the resources fairly. The system then exchanges information with the environment: the exchange of inside information "does not make noise!" The group adapts to the environment. The maximum entropy is the most favorable condition for the adaptation of the group. If we call C1, C2, ..., Cn the chromosomes of a population, p1, p2, ..., pn the probabilities of survival of various chromosomes, the random number is in this case the information carried by each chromosome Ci: log2 . 1/pi. As we know the maximum entropy occurs in the theoretical situation in which all the chromosomes carrying the same information, ie have the same chance of survival. In essence, the situation of individual selection corresponds to a situation of random number highly imbalanced, with high probability for individuals as "selfish" and low for others, and thus far from maximum entropy. The situation of maximum indecision, however, in which all the chromosomes have comparable probability is of maximum entropy. And to this tend chromosomes of the family when they communicate with each other and then not implementing strategies selfish, that the long penalize all, and this depends more or less directly from information that the chromosomes of the family can share! I do not know if this can explain the real evolution. However after Dawkins and after distinctions and the criticism of pure Darwinism, this might be a way to fly: that is investigating the communication between individuals, groups and environment: study the information that each population brings with it and the link between this and the randomness of mutations within the group. This approach is formulated at the level of conjecture, applied to a "virtual" evolving world, in which cooperative behavior can emerge and not just selfish; provided that the evolution is not blind but communication between individuals. From a social point of view, as to the state of permanent conflict. the "bellum omnium contra omnes" of Hobbes, makes a bank outside the legislation, which regulates mutual relations, thus to evolution blind and selfish, are a barrier limits of behaviour that emerge internally: the presence or absence of communication among individuals around or inhibits the selection selfish; and in this second case, the group adapts to the environment, since the entropy goes to the maximum. Entropy is also used as a measure of disorder of a system: in the sense that a state ordered (simple) is 'easier to understand (and communicate) that a disordered one. We formulate the following:
Entropic Hypothesis: adaptation to the environment of an organism, is in the direction of increasing entropy with the complexity.
8 Approach to simulation
We used the
simulation model of Holland.
Genetic Algorithms are procedures, adaptive, aimed at solving problems of
search and optimization: strategies in practice are seeking the maximum point
of a certain function, when this feature is too complex to be maximized with
fast analytical methods and it is unthinkable to explore the solution space
randomly. The GA selects the best
solutions and recombine in different ways so that they evolve towards a maximum
point. This function is called fitness function . The term has different
meanings in different tones: "adaptability," "biological
success", "competitiveness", etc. .. The model simulates the
evolution of a population of n strings, the strings are made of bits and are
generated randomly. The number of possible strings, according to the
combinatorics, is equal to 2l
and represents the space of solutions to the problem. Each string (called genotype) represents the
binary encoding of a candidate solution (called phenotype).
For each genotype already
the initial population P (t = 0) at t = 0, is associated with a value fi = F
(gi) which represents the individual's ability to solve the problem. To
determine the value of adaptability, the fitness function receives as input a
genotype, decodes the corresponding phenotypes and tests it on the given
problem. Completed the assessment phase
of the individuals of the population at time t, P (t), it is generated a new population at time t +1, P (t
+1), of n new candidate solutions: this
is achieved by applying the operators selection , mutation, crossover and
inversion.
Interaction with the environment. The interaction with the environment play a fundamental role in the evolution. A simulation schema to bring the system to the solidarity may be the following one. Let a generic system constituted by individuals that may enter a series of resources Ri. Each individual can take the number of resources he wants, necessary for the survival. Being each resource organized in more sections (t), the individual may acquire, for each resource, a number of section x ≤ t to his liking. Since the environment interacts through the fitness function, this may penalize the individuals: this happens if, in section acquisition, they surpass a given number q
ƒ = max{ ∑i ( Ri[q]-Ri[x- q] ) } (a)
Appendix
Simulations of interaction between robots. See [60]
Figure 1 shows the results of our simulation.
This operates on the interaction of two robots whose behaviour
emerge from a simulation of various epochs, and many generations, on many (100)
individuals, several (20) parents, and some (5) offspring; using as selection
method ‘Elite’, and 1000 simulation steps for each trajectory. The simulation
involves a neural structure that is the control of the robot, and runs as just
described, including the fair-unselfish policy. The goal of the
evolution is the adaptation to the environment: each robot attempts to explore
the space, in search of resources for its nutrition, and so, after various
epochs of simulation, converging toward a stationary path. The competition is
realised with the presence in the same environment of two robots: the
application of the fair-unselfish policy allow both the robots to converge
toward a cooperative stationary paths that allows to feed both robots.
Consider the strategy Tit for Tat and the need for rules that emerge
in complex organisms.
You would like to highlight the effects of the interaction of individuals, even if artificial,
on limited resources and how it may seem a cooperating behavior. What should be
noted is that the trajectories of two robot through both all five feeding areas. In essence,
the winning pattern above the selfishness of one of the robots and end up being collaborative,
allowing each robot to feed all these sources. There are not exclusions and hoarding of natural resources.
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