Edmund Chattoe (1998) 'Just How (Un)realistic are Evolutionary Algorithms as Representations of Social Processes?'
Journal of Artificial Societies and Social Simulation vol. 1, no. 3, <https://www.jasss.org/1/3/2.html>
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Received: 15-Feb-98 Accepted: 13-Jun-98 Published: 30-Jun-98
2 Orthodox is used here in the sense defined by Nelson and Winter (1982).
3 There are, however, some fascinating exceptions (Bagehot 1887).
4 The earliest paper satisfying this definition is generally agreed to be Alchian (1950).
5 Although genetic "fitness" is typically associated with numbers of live offspring, the tendency to reproduce as often as possible may be an effect as much as a cause of evolutionary selection, which merely requires persistence. Although the first species that began to reproduce rapidly would gain an advantage against species which did not, no species could gain any further advantage once rapid reproduction was universal.
6 This is a simplification. There are actually several processes by which genetic mixing and mutation take place, corresponding to various things which can happen to the chromosomes (Weaver and Hedrick 1992: 87-94). In asexual reproduction there is no genetic mixing. However, asexual reproduction is limited to simple creatures and sexual reproduction can itself be seen as an evolved mechanism by which genetic mixing can take place if it is beneficial.
7 The validity of the assumption that social behaviour is no more than the sum of individual interactions remains contested in social science. Although economics favours a purely individualistic interpretation, sociologists sometimes appear to argue that norms and other social influences are more than the sum of individual mental contents.
8 We can thus see the ability to make these attributions and cultural (rather than genetic) transmission of behaviours as evolved mechanisms which reduce the wastefulness of the evolutionary process. Instead of "throwing away" a whole individual, it is possible for that individual to both identify and modify particular aspects of behaviour that are persistence threatening. We would expect these features to be both a cause and a consequence of increasing complexity. As individuals become more complex and the potential for diversity increases, it becomes more wasteful to "throw away" whole individuals. At the same time, self awareness and cultural transmission dramatically speed up processes of experimentation in relation to the environment, which were previously restricted to one "try" per lifetime.
9 Note that progressive increases in complexity are not implied. That depends on whether a species with a given level of complexity persists robustly against all simpler organisms and their subsequent developments. So far, humanity appears to be in this position, but only so far.
10 The traditional example is that the children of hard working blacksmiths might be born with extra muscles. But equally the children of amputees might be born with missing limbs.
11 Lamarckism arose before genes were discovered. It persisted, rather uneasily, when there still appeared to be a simple mapping between genes and "traits" like blue eyes, but has not survived the discovery of detailed biochemical mechanisms of development.
12 They may even be meaningless (Tintner 1941a, Tintner 1941b) or incalculable (Thomas 1993).
13 It will be recalled that the evolutionary process implies nothing about whether competitive or co-operative strategies will tend to persist more effectively (Kropotkin 1939).
14 Conceptual smoothness merely implies that we do not need to postulate any uncaused causes or mysterious jumps. It is no more than "good science" writ large. Although we will never know what went through the mind of the person who invented the wheel at the crucial moment, we can be confident that it was something to do with what they knew and had experienced, seen or perhaps been told. Although the discovery of fire may have caused an enormous discontinuity in social practices, there is no conceptual discontinuity implied by its discovery and application.
15 These were also chosen because they are the techniques used in most of the evolutionary modelling done in social science to date. Other related techniques include Evolutionary Programming (Fogel et al. 1966, Fogel 1991), Evolution Strategies (Rechenberg 1973, Schwefel 1981) and Simulated Annealing (Davis 1987).
16 Random generation is compatible with the conceptual smoothness of the evolutionary process in obliging the population of individuals to begin in a state of maximum disorder.
17 This interpretation has however been challenged by De Jong (1992).
18 This analogy raises an interesting point. It is assumed that the meat yield of cows is a purely genetic matter, just as it is assumed that an instrumental GA process can have a fixed fitness function attributed to it, as it clearly can in the simplest cases. If it turns out that meat yields are affected by the "social" organisation of the herd or that the fitnesses of population members actually affect each other, then the instrumental approach is an empirically invalid choice.
19 One reason for this is that the tree representation translates directly into a nested bracket representation suitable for list processing languages such as LISP.
20 There are GA techniques which make use of variable length representations but they are typically unwieldy. See Harvey (1992) for further discussion.
21 It is hard to say conclusively that a GP tree is more expressive than a GA string, since any arbitrarily complex encoding for the GA can be hidden away in the fitness function. However, it is intuitive that the GP tree and the fixed "meanings" of its operators taken together are likely to be more economical than such an arbitrary encoding.
22 It is clear that this is a drawback for an instrumental GA which should ideally solve a problem as quickly as possible. However, it may not be a drawback for a GA used descriptively since it corresponds prima facie to human behaviour in focusing on important issues first. In practice, this analogy does not bear close inspection and other unrealistic aspects of the simple GA, such as the exogenous fitness function, are far more damaging to its use as a descriptive model.
23 This can either mean that the program is "self executing" or that a process of interpretation and execution is internal to the individual. Either interpretation also requires some distinction between the genotype and phenotype.
24 We can distinguish three interpretations of the robot controller example. In the first, both the environment and the controller are "abstract" and exist purely within the GP. In the second, the controller remains "abstract" but there is also an attempt to simulate a real environment in which the controller will evolve. In the third, the GP is actually a program inside a real robot, operating in a real environment.
25 Social science seems to give inadequate general attention to differing degrees of the social. Although it rightly has no interest in situations where a single individual interacts purely with a physical environment, it offers little by way of guidance as to how we might expect behaviour to change as we move from agents interacting predominantly with an environment to agents predominantly interacting with each other.
26 An interesting paper using this approach, applied to zoology rather than social science, is Koza et al. (1992).
27 This description follows Nissen (1993).
28 The structure of individuals could be subsumed straightforwardly into a GP, but not the operations which are carried out on those individuals.
29 For the technicalities of this process, the reader is referred to Holland et al. (1986). Rules which fire and result in a good outcome "share out" the positive feedback, as do those which result in negative feedback. This sharing discourages bloating of the rule base.
30 A GA which generates diverse populations to solve problems collectively is discussed in Smith et al. (1992). It is an interesting question whether the syntax of GP makes diverse populations redundant, impractical or neither.
31 Although the GP may produce a program that is equivalent to (rule1 AND rule2 AND rule3), it has no endogenous process to ensure hierarchical comprehensibility, so even if the operators are designed for easy interpretation, there is no guarantee that the overall structure will be easy to interpret. Furthermore, as the depth of the GP tree increases, so does the number of equivalent trees. These can be seen as a potential drawbacks of GP representations which may be addressed by current research into program modularisation through ADFs (Koza 1994).
32 The prevailing view that bounded rationality involves no more than applying rational principles to the process of cognition seems both incompatible with the bulk of what Simon wrote and incoherent on closer inspection, for precisely the reasons that Simon gives.
33 In addition to the obvious point that these models are empirically very implausible!
34 An example is provided by the history of atomic theory, where compounds were explained in terms of molecules, composed of atoms, composed of neutrons, protons and electrons, composed of quarks, composed of ...
35 There may also be a signal extraction or credit assignment problem when an agent co-varies rules and meta-rules while trying to make sense of the environment.
36 For a simple illustration, consider an individual who has a gun with a laser sight trained on him by a distant assassin. The assassin is so far away that they cannot be seen directly, but the reflection of the laser sight can. If the reflection can be seen near the target, the rational action is to take cover, otherwise it is to try and move to locate the assassin. Unfortunately, if the assassin trains the laser on some part of the victim's forehead, which is the best way to be sure of a kill, the only way the victim can see it is by holding up a mirror which, we suppose, is of such a shape and size that using it to look for the reflection will block the beam. Even if the blind spot is tiny, the victim will certainly die, despite having a rational decision process, because they have no way of simultaneously or sequentially observing the location of the reflection and the effect which the mirror is having on the beam. Admittedly, this example relies on an assumption about the shape of the mirror, but recall that its object was only to explain why even the smallest blind spot can destroy the possibility for rational action as economics defines it. In fact, one could argue that the shape of the mirror is consistent with defining some part of the forehead as a proper blind spot in the first place. A blind spot is more than somewhere you can't see when not looking in the correct direction! (Even if the victim had a second mirror of the same size and shape, and was very dextrous, they still could not use that mirror to observe the effect the laser was having on the back of the first mirror because it would then be the second mirror blocking the beam.) These ideas are developed further in work on "autopoietic systems" (Varela 1979, 1991).
37 However, they can be justified in other ways, for example as biologically evolved competencies. Unfortunately, this seems rather an admission of defeat as far as social science is concerned.
38 This raises another interesting issue. Although one agent is hampered by the difficulties of obtaining adequate information about another, it does not suffer from any conceptually necessary blind spot in observing other agents. (It is an open question whether the blind spot of the first agent will definitely impair its understanding of the second.) It is truly the case that others may know us better than we know ourselves from a logical point of view!
39 The same logic applies to any models constructed by the social scientist. This view also implies a coherentist rather than positivist notion of truth.
40 Note that common knowledge of this shared correct model, which enables it to form the basis for rational action, is an even stronger assumption than that agents all merely happen to have the correct model (Parikh 1990).
41 There is an obvious but very important difference between the assumption that individuals do what they intend to do and that what happens is what agents intend. The fact that it is possible to miss this difference is illustrated by the argument between Alchian (1950, 1953) and Penrose (1952, 1953).
42 Although we are concentrating on cognitive irreversibility, it is obvious that irreversibility relating to autonomous physical processes (Georgescu-Roegen 1971) can also be part of the same framework.
43 As Nelson and Winter (1982) have pointed out, orthodox economic theory struggles with genuine novelty. We will have return to this issue as the same problem appears to beset simple evolutionary algorithms.
44 An example is provided by the discussion of "big players" in Koppl and Langlois (1994).
45 Friedman (1953) argues that the assumption that firms are profit maximisers can be justified by the fact that the market will tend to eliminate those firms that are not. This pseudo-Darwinian argument is still widely believed, despite being convincingly refuted by Witt and Perske (1982) and Chiappori (1984) among others. Ironically, the error in Friedman's reasoning is one that originates with Herbert Spencer over a hundred years ago!
46 Although the abstract theory of the market may imply a universal framework of law standardising the behaviour of firms, a more detailed view reveals a rich structure of compliance, evasion, detection, political action, punishment and resistance. A question for the future is the extent to which more or less generally agreed but not universal practices can be said to constitute an external teleology and whether or not different degrees of agreement can be detected in the dynamics of different social processes.
47 The minimal condition for market persistence is rather similar to that in biological systems, that the organism should "cover costs". Discussion of the widely held but mistaken belief that evolution produces optimal behaviour can be found in Hodgson (1991).
48 There are some extremely interesting and widespread developments in industrial organisation which can be considered in evolutionary terms. The first of these is the existence of firms which develop by merger and buy out rather than pure production. The second is the existence of franchises and chains which really do reproduce branches whose operating practices may or may not be appropriate to their locales. These chains have to "trade off" economies resulting from common practices against loss of sales from local idiosyncrasies. They also have to consider the "carrying capacity" of the environment in siting their new branches.
49 We should not perhaps presume on this issue, economic socialisation is still inadequately explored and although long run historical analyses of the nature of the firm do exist, these are largely discursive.
50 Another interpretation is that if firms know that the encoding is common and that the fitness function is one to one, they can work back from what firms did to what their GA string must be, although this raises issues about timing. Perhaps telepathy is a more behaviourally plausible assumption after all!
51 If we assume sociable agents then intentions are transmissible, but it is not clear how accurate measures of utility could be transmitted, even with the most active desire to communicate. Transmission of information about money amounts is obviously not helpful!
52 It should be noted that not all game theoretic evolutionary models are based on replicator dynamics.
53 Other differences between economic and biological games are discussed by Selten (1993).
54 One could view replicator dynamics as a purely instrumental technique on this basis. Once it is assumed that all individuals are utility maximisers and it is utility which determines propagation, the attainment of game equilibria is a foregone conclusion. If, as many users of replicator dynamics argue, its important contribution is to show which equilibrium occurs, then it is no longer possible to argue that behavioural assumptions about the process are irrelevant.
55 One could diagnose the convergence problems in the Arifovic models knowing nothing at all about its interpretation.
56 There are a number of models based on GA which avoid one or more of the shortcomings of the Arifovic models, for example Curzon Price (1997), Lomborg (1992) and Vriend (1994). Space considerations preclude their detailed discussion here, but some of the comments on GP models will also raise issues appropriate to those using GA techniques. In what follows, GP terminology will be used for simplicity.
57 Edmonds (1997) uses a population of 40! Two additional comments can be made on this observation. The first is that because most of the cost of executing an instrumental GP comes in evaluating strategies, the actual coding of the GP involves deliberately not evaluating duplicate strategies, but using a hash table to weed out strategies that have already been tried (Koza 1992a). Even using this technique cannot avoid the wastefulness of strategies which are semantically equivalent but syntactically different. The second is that the number of strategies which can be "borne in mind" is far greater in a firm, where these would correspond to the views of particular individuals in the firm. The weights of strategies could then correspond to the number of people arguing for a particular view or their importance in the hierarchy.
58 Rank based selection has the advantage that selection pressure does not drop as the population converges (Whitley 1989). It is an interesting question whether this also applies in social situations.
59 This is trivial for a static fitness function, but not for a descriptive simulation in which fitness may change.
60 This issue is discussed from an instrumental perspective by Reeves (1993).
61 One interesting possibility is that common knowledge may be mimicked by the fact that individuals tend to project their own models onto others. If, in fact, everybody did have the same model, this would be as good as common knowledge for the purposes of deciding what to do. Of course, it would fail miserably if models differed significantly. Perhaps this is why people have wars over religion rather than food.
62 In implementing a version of the Dosi et al. (1994) model, I followed them in assuming that firms selected strategies probabilistically on the basis of cumulative profits. When the strategies simply consisted of fixed prices rather than GP trees, all the firms quickly learnt to set the maximum price they could and rapidly priced themselves out of the market. Although there was an equilibrium with everyone charging the maximum permissible price, it was never observed because it required that all firms initially charged that price and did not deviate. This is an example where the fitness function for firms was not a complete representation of the restrictions on the market and thus prevented any firms from persisting. In this case, firms valued positive profit without limit but placed no value on market share at all.
63 It is also the case that retention of profits does make firms far less susceptible to whatever discipline the market imposes and also perhaps more able to influence the terms on which market discipline is applied. This point seems to receive inadequate attention from evolutionary market apologists. Since this escape from discipline reflects the very success of firms, perhaps it is true as Marx suggested that competition, if not capitalism contains the seeds of its own destruction!
64 They can be assigned the fitness of their parents providing that firms do not have an infinite memory for cumulated profit.
65 The relevance of qualitative physics is illustrated in a paper by Sims (1991). This describes a simulation in which artificial organisms constructed of rigid blocks and joints are "evolved" to perform simple tasks like "capturing" food. Rather than seeing the properties of joints and blocks as attributes of the organism, as classical Artificial Intelligence might, these attributes are modelled as functions of environmental factors such as "gravity" and "mass". The result is that the behaviour of arbitrary evolved combinations of blocks and joints is always defined.
66 This conclusion is also reached in the Artificial Life literature (Langton 1989, Langton et al. 1991, Langton 1993). Complex environments have another interesting effect. Because evolutionary algorithms are very good at optimisation, they can often produce strategies which, in being self-evidently silly, reveal something about the inadequacy of the environmental specification. Developing the simulation thus becomes a co-evolutionary process, with behaviourally implausible strategies sometimes revealing unrealistic assumptions about the agent and sometimes about the environment.
67 This distinction is often badly muddled in replicator dynamics models. If individuals just are their strategies then there is no difficulty, except that this is a very unrealistic view of individuals. If there is a difference between the strategy and the actions it produces, then strong knowledge assumptions are required to avoid worrying about how inferences can be made from actions back to strategies.
68 In one firm where I worked, employee research group membership was not listed in the internal phone directory so nobody outside the company could get an overall picture of the amount of research being done in different areas!
69 In Chattoe and Gilbert (1997), agents learn how to budget by evolving budgeting plans individualistically, but they are also able to observe the consumption patterns of other agents. The result is a co-evolution of effective budgeting plans and stratified lifestyles based on income.
70 There is an analogy here with the highly instrumentally effective Island Model class of Genetic Algorithms in which relatively small populations evolve in parallel, but transmit their best strategies at random to other populations from time to time (Gordon and Whitley 1993).
71 Another way of seeing the issue of profits reducing the competitive pressure on firms is to ask whether profit tends to "evaporate" or not. Individuals can never achieve more than a certain level of energy or wakefulness and this continues to drain away whatever they do.
72 This program is proceeding for instrumental GP in the development of ADF techniques (Koza 1994) which may provide interesting insights which descriptive models can use.
73 There is an obvious role here for the sort of computational organisation theory models devised by Carley and others (Carley and Prietula 1994).
74 Large trees are behaviourally implausible as well as almost impossible to interpret. An additional danger, suggested by the Dosi et al. results on price tracking is that a large GP tree may just become a lookup table for the state of the present environment. There has been little research so far on pulling GP trees that are apparently successful out of one environment and putting them in another.
75 Another consequence of semantic analysis is the possibility of analogical reasoning. If a sub tree has a "meaning" attached to it, like "total costs" it becomes possible to substitute one sub tree for another in a way that is somewhat directed, as it relies on similarities of meaning. This also applies at the level of whole GP trees, which may only produce a number as output, but it is the use to which the organisation puts that number which determines its meaning. Thus a tree that solves one environmental problem, may be more likely to solve an analogous problem.
76 Work of this kind could make use of research such as that by Sun and Bookman (1993) on the integration of neural and symbolic processing.
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