The artificial worlds approach to emergent evolution

BioSystems, 23 (1989) 247--260

247

Elsevier Scientific Publishers Ireland Ltd.

The artificial worlds approach to emergent evolution Michael Conrad ~ and Mateen

M. Rizki b

•Department of Computer Science, Wayne State University, Detroit, MI ~8~0~ and bDepartment of Computer Science and Engineerin 9, Wright State University, Dayto~ OH ~5~35 (U.S.A.) Artificial worlds models of evolutionary systems are computer models that map the essential logical structure of ecological systems, defined as self-sustaining biological organizations. The artificial world comprises an artificial environment, with mass components, energy input, and physical states. It also comprises artificial organisms, including a genome, a phenome, and a (developmental) map t h a t connects the genome to the phenome. Mass components are cycled and space is limited. The evolution process results, as in nature, from genetic variation combined with natural selection imposed by the finiteness of the environment. The selection criteria (fitness values) are not imposed, but r a t h e r emerge from the interactions of the organisms with each other and with the environment. The dynamics at the population level also emerges from these basic interactions. In this paper we describe the comparative properties of the EVOLVE family of artificial worlds models.

Keywords: Computer ecosystem models; Computer evolution models; Artificial worlds.

1. Introduction Over the past 20 years we have developed a family of high resolution interactional models to study ecosystem evolution (Conrad, 1981; Conrad and Pattee, 1970; Conrad and Strizich, 1985; Rizki and Conrad, 1985). These models are not intended to represent any system in nature in detail. Rather, they abstract those interactions that we believe to be essential to support a self-sustaining evolutionary process. As with naturally occurring ecosystems the generality of conclusions drawn from the study of such models must be tempered by putting them in the context of the particularities of the system. This is the problem of induction in biology. And it is why comparative study is so important. Unfortunately there has not yet been enough artificial worlds modeling of evolutionary ecosystems to make the broad comparative study' that will eventually be needed. But we think it is useful at this point to make a comparative review of the three model families with which we have

worked. Hopefully this will suggest some categories for comparison, and help hasten the day when different groups of investigators can join together to make a more wide ranging comparison and contrast. We are encouraged, in fact, by an incipient literature (e.g. Hogeweg and Hesper, 1983; Papentin, 1973; Rada, 1981). 2. W h a t is to be represented? Like art, artificial worlds models can follow nature, or they can follow de novo inventivity. Our preference has been to abstract essential features of nature. Simplifying assumptions and compromises are obviously necessary. We cannot represent organisms and their interactions in great detail, and at the same time represent as many organisms as occur in a natural ecosystem in nature. The computer ecosystem is a virtual system built on top of a programmable machine, and it soon becomes painfully clear that many interactions that are simply and efficiently embodied in

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248 organic materials can not be represented in a programmable machine without a major expenditure of computational resources. The chief ground rule is that an artificial worlds ecosystem should be a self-contained microcosm, capable of autonomously generating its own evolutionary development. In principle it could be closed to the modeler after its creation in the same way that a real microecosystem enclosed in a laboratory flask might be. The population and ecosystem dynamics should emerge from simple rules of interaction governing the behavior of organisms rather than being imposed by fiat by the modeler. These rules should themselves be under genetic control and subject to evolution to a degree that approaches as near as practical to the genetic control exerted by organisms in nature. Constructs such as fitness should not enter into the model. They should emerge in the same way that fitness emerges in nature. And if fitness is an unclear or hard to define concept for natural systems it should be unclear and hard to define for the artificial world as well. If species are difficult to identify in nature they should be difficult to identify in the model as well. In order to satisfy the closure requirement models must incorporate a number of basic features. A minimal list includes: (1) Multiple layers of biological organization. (2) Local interaction rules, as opposed to population level fitness functions. (3) Spatial aspects of the environment represented in a meaningful way. (4) A well-defined genetic system capable of sustaining a myriad of gene combinations and producing gradual variations in the structural and functional features of the individual. (5) Detailed modeling of energy and mass flow through the system. (6) Graded environment with respect to energy and mass, allowing individuals to exploit resources in a realistic fashion. (7) Cooperation within each layer of

organization possible but not explicitly encoded. (8) Multifaceted physical environment, including local variations in attributes such as temperature and light intensity, subtle changes in relationships between physical boundaries of the space, and global variations in environmental features. (9) The models abstract organization across different time scales (molecular, organismic, populational, ecological). (10) Potential to evolve is included but the direction is not specified. The original model was called EVOLVE (Conrad, 1970, 1981; Conrad and Pattee, 1970). We will denote this by EVOLVE I, and the two subsequent models by EVOLVE II (Conrad and Strizich, 1985) and EVOLVE III (Rizki, 1985; Rizki and Conrad, 1985,1986). EVOLVE III, in particular, comprised a rather large family of models. We will briefly describe the extent to which each of these models represents the above properties and some qualitative features of its behavior, with particular attention to the key issue of emergence. 3. The models Ecosystems are composed of a complex fabric of subtle interactions and intricate physical relationships. The emphasis in the artificial worlds approach to modeling is to capture a complete swatch of this cloth, as opposed to unraveling the individual fibers. This approach to modeling is the common thread that bonds the EVOLVE family together. As indicated above, however, we are here drawing attention to the contrasts within the family rather than to the contrasts between the artificial worlds approach and more traditional approaches. Let us begin with a thumbnail characterization of the three main models. Evolve I

This system is built up from one-dimen-

249 sional "organisms" operating in a onedimensional environment. The environment is a sequence of places in w h i c h the organisms can carry on their activities (such as feeding). It is organized as a circle to eliminate edge effects. Each place has a state (say A or B) and some number of mass units. The number of mass units in any given place can vary, but the total number in the whole system is strictly conserved. Each organism consists of a genome and a phenome. The genome is a sequence of bases. These are translated to traits according to a doublet code. The translation is rather simple, basically with a mapping of codons into a repertoire of six traits. Organisms may be thought of as automata of sorts that operate over a number of places. As they move from place to place in sequence they change their state, each state corresponding to one of the traits. Actually traits are routines, including routines for matching the environment, looking for an organism with which to exchange mass (symbiosis or predation), looking for an organism to exchange genetic material with (like bacterial conjugation), allocating mass units to self-repair, as well as a routine for controlling the expression of the genome (similar to enzyme induction). There are also evolvable phenotypic codes for species specificity, symbiotic specificity, and dispersal of offspring. The routines are fixed, but often they are used in novel, unexpected ways. Organisms can withdraw mass units from a place in the environment when their state matches the state of the place. For example we can think of the place as being wet or dry, and the organism as being in a physiological state which is suitable to a wet or dry environment. If it goes into a wet functioning state and the place is in fact wet, resources can then be withdrawn. However, the model is so constructed that all the organisms appear to function in parallel. This is achieved through a multipass mechanism. The matching of the environ-

ment is done in one pass and the allocation of resources in a second pass. How many resources are allocated for a match depends on how many other organisms matched the place in question. When an organism accumulates a sufficient number of resources it reproduces. Reproduction is accompanied by various kinds of mutation and possibly by genetic recombination (actually conjugation). Since the species specificity codes are genetically controllable the sexually defined species are not imposed on the model. If species emerge they do so through the process of evolution, not through the design of the investigator. EVOLVE I was written in the LISP language. Numbers played a very unimportant role in the operations of the system, except for some accounting that contributed to the resource allocation algorithm. The system exhibited a great deal of interesting coadaptive behavior, led to new insights into the relationship between mass cycling, population dynamics and gene structure, and into novel uses of given routines. Some of these unexpected uses were due to the fact that the control capabilities of the organisms were limited, and as a consequence they used sexual, symbiotic, and inducibility routines for unexpected ecosystem regulatory functions. In some cases these were advantageous to the population in terms of biomass accumulation, but disadvantageous to the individual. A number of mechanisms contributed to this type of coadaptive evolution. When mass cycling was slowed down, population oscillations became more marked. At the low point of an oscillation a great deal of "genetic junk" could proliferate. As the population grew and resources became scarce again this junk was pruned. Some of the evolution of coadaptive regulation was actually a byproduct of this interaction between population dynamics and gene structure. The number of experiments performed with the model was inadequate to explore its full range of behavior due to the scarc-

250 ity of computer resources in the 1960s. The system showed, however, that behavior reminiscent of evolution in many respects could be achieved, and that artificial worlds models could lead to discoveries about biological evolution. The model also revealed many aspects of biological evolution that are tough to duplicate in an artificial worlds setting. Evolve I I

This system incorporated many of the same basic ideas as EVOLVE I, but was numerically based, much simpler, and much more efficient. The environment was reduced to one place characterized by three properties: light intensity, temperature, and number of mass units. The light and temperature could vary according to any regime imposed by the investigator. The number of mass units free in the environment depended on the cycling dynamics of the model organisms. As in EVOLVE I, organisms consisted of a genome and a phenome. The genes were represented by numbers, from 0 to 9. There were three types of genes, corresponding to three traits: temperature of optimum function, light intensity optimum, and aging rate. Each gene also had a decimal part, ranging from 0 to 9. This represented what we termed evolutionary amenability (or degree of gradualism). A gene with a high amenability would be more likely to mutate to a similar gene value, whereas a gene with low amenability would be more likely to jump to a very different value. An organism whose light and temperature optima matched the actual light intensity and temperature of the environment could withdraw more mass units than an organism that makes a poor match. But how many units are withdrawn by a single organism depends on the performance of all other organisms. As in EVOLVE I a multipass algorithm allows all the organisms in effect to operate in parallel. Genetic variation in this model was restricted to simple point

mutation. The mapping of genome into phenome was reduced to a fairly trivial mapping of gene values into phenotype values. As in EVOLVE I mass units are strictly conserved. EVOLVE II was written in PLI, and extensive experiments were performed. Its evolutionary potentialities were much less rich than those of EVOLVE I. Nevertheless it showed some interesting behaviors. Two species, defined by different aging strategies, often emerged. These two species coexisted in precisely the same environment (since there was only one environment). Conceivably it could be argued that they operated in different niches, since the different aging strategies meant that they used the environment differently. But this is stretching matters. This was a clear case of a violation of the competitive exclusion principle. In some cases genes that coded for the dominant species evolved low amenabilities, making it more likely to switch to the alternative (faster) reproduction strategy. This surprising result demonstrated that gene structures can evolve which are advantageous from the standpoint of the lineage, but not from the standpoint of individual offspring. Experiments were performed to test how the stability properties of the system depended on the uncertainty of the environment (the adaptability-stability experiment). The bimodal aging strategies played a controlling role here. The more uncertain environments tended to favor long lived species, and therefore actually reduced adaptability. The results with the more realistic EVOLVE III, to be described shortly, were different and corresponded to actual laboratory experiments. Later we will consider the methodological significance of this difference. Evolve H I

Evolve III is composed of three nested models, each corresponding to a different layer of biological organization. The outermost model abstracts features of a complete

251 ecosystem. The physical environment is represented as a two-dimensional grid of places. Each place is subject to variations in physical attributes, contains a variety of types and numbers of resource units, and is physically adjacent to a set of other environmental places. In addition, each place contains a collection of organisms that compete for the limited supply of resource units. As in Evolve I and II, each organism has a phenome and a genome. The phenome consists of a fixed collection of traits that function to control the selection of resources, metabolic activities, reproductive strategies, selection of environmental locations, tolerance to environmental variations, and territoriality. Individual traits are coded by gene complexes and the collections of genes coding individual traits can overlap among different traits. This representation of many-to-many mapping allows the system to adjust traits while maintaining a balance among related traits. The model of a gene abstracts the organization of DNA by representing the linear sequence of nucleic acid bases as a simple bit string. Every pair of bits corresponds to a single nucleic acid base. To abstract the process of protein folding, special regions of the sequence are designated as primarily responsible for determining the gene level of function. The codons located in these regions are assigned a numeric value and a weighted sum is formed to calculate the overall gene function. Additional regions are also represented in the gene model that are used to make small adjustments to the final gene value. The gene sequence contains noncoding regions that serve as a scratch pad where genetic garbage can accumulate due to point mutations. Each gene is modeled by a bit string of length 320, and each organism is composed of approximately 40 genes coding 1 5 - 2 0 traits. The current implementation can support approximately 2000 organisms of this complexity in a 16 × 16 grid of places.

The model is written in Fortran and the simulation is cast in a discrete events framework. The activities of the individual organisms (resource collection, waste disposal, migration, reproduction, and death) are modeled as events. The time of occurrence of any event is determined from a set of random distributions that are parameterized using phenotypic features of the individual undergoing the event. Consequently, as mutations alter the genetic structure of the organism, and hence the phenotype, its behavior is also transformed. The use of the discrete event modeling technique frees the modeler from the need to incorporate complex ranking functions to allocate resources to individual organisms. Since time is conceptually continuous, organisms feed for intervals that may or may not overlap with other organisms within the same environmental location. In the absence of overlap no conflict occurs, so no ranking is required. If two individuals do compete for resources at the same place and at the same time, then their match to the environment and a comparison of the degree of territoriality is used to parameterize a conflict resolution distribution to select a winner. Thus, all conflicts are resolved locally between pairs of individuals. Several versions of EVOLVE III were constructed. Version 1 was implemented in PLI on an IBM 370 type machine. This incorporated multichromosomal genetics and sexual reproduction. A microcomputer version was implemented in C. At the time, however, microcomputers were not fast enough to generate enough results for a thorough experimental exploration. Our main version (called version 2 in the tables) was implemented in Fortran on a VAX 780. The features of this model (which did not include sexuality) are outlined above. We performed extensive experiments on version 2. The process of tuning the model was lengthy and instructive. The powerful effects of many physical constraints on

252 organisms which might initially be viewed as not particularly i m p o r t a n t to the evolution process w e r e exposed. If an organism could r e d u c e its metabolism a r b i t r a r i l y , for example, the biota b e c a m e d o m i n a t e d by what in effect are ghost organisms. Such learning t h r o u g h t u n i n g has been a general feature of our experience with the E V O L V E family. In E V O L V E I a too generous assumption about life span led to a population of organisms t h a t allocated all t h e i r r e s o u r c e s to self-repair, in effect opting for childless immortality. The t u n e d model yielded an e x t r e m e l y rich v a r i e t y of organisms, with a g r e a t deal of polymorphism at both the p h e n o t y p i c and genetic levels. E x t e r n a l l y d i r e c t e d traits,

such as light and t e m p e r a t u r e optima, t e n d e d to follow the e n v i r o n m e n t . But internal traits, such as d e v e l o p m e n t a l time, often bifurcated, indicating t h a t d i f f e r e n t lineages w e r e following d i f f e r e n t strategies. How m a n y typological species t h e r e are in the s y s t e m depends on w h e t h e r such internal t r a i t s split into different s t r a t e g i e s independently. We are c u r r e n t l y analyzing this issue. E x t e n s i v e e x p e r i m e n t s w e r e performed on m u t a t i o n controls to see to w h a t e x t e n t the s t r a t e g y of evolution could itself be subject to selection. In g e n e r a l t h e distribution of m u t a t i o n r a t e s becomes quite chaotic, s u g g e s t i n g the p r e d o m i n a n c e of hitchhiking effects r a t h e r t h a n optimization. We can impose a m u t a t i o n r a t e on one

TABLE 1 (PART A) EVOLVE family features. EVOLVE I

EVOLVE II

EVOLVE III

Varying length string Constitutive/inducible Possible

Scalar Constitutive No

Varying length string Constitutive Yes

Genes/organism Alleles/gene

Open 16

Fixed 10

64**5

Mutation mechanisms Rate control Types modeled

Codon usage Base substitution

No internal control Base substitution

By individual genes Substitution/frameshift

Yes No Yes No Yes Yes

No No No No No No

Yes Yes (Version 1) Yes (Version 1) Yes (Version 1) Yes Yes

One to one Codon degeneracy Yes

One to one One to one Yes

Many to one One to many Yes

No

No

Yes Yes Yes

No No No

No Yes Yes

Gene organization

Representation Function Non-coding regions Size of genome

Open

Chromosomal variations

Conjugation Recombination Crossover Inversion Gene duplication Gene addition/deletion Genome-phenome map

Genesfcrait Traits/gene Gradualistic Phenome

Emergent traits Novel use of traits Structurally open Cooperative effects

No

253

population and allow the mutation rate to evolve in a competing population. Amazingly the freely evolving population is often unable to maintain a mutation rate which is as effective from the point of view of evolution as the one imposed by the investigator. The most extensive work was done on the adaptability-stability experiment (see next section}. EVOLVE III had without doubt the richest dynamics of the three models; it showed little indication that it would ever settle down into an equilibrium configuration. 4. The models

compared

Table 1 (parts A and B) summarizes the model representations of biological structure and function within the EVOLVE family as described in the previous section, highlight-

ing common and contrasting features. Table 2 categorizes the dynamic behavior of the models using terms commonly employed in the description of natural ecosystems. Our objective is to illustrate the relationship between model features and model capabilities. To do this we use a somewhat subjective ranking within the model family. Thus "high complexity" means high relative to the other models in the family, not relative to nature. The rankings summarize our judgements about the models based on extensive experience with them. Due to the fact that the models were studied on different machines at different times it would be difficult to quantify these comparisons. Let us first consider the categories listed in Table 2, starting at the highest level of ecosystem organization. At this level we are interested in the dynamic properties of

TABLE I ( P A R T BI EVOLVE h m i l y ~ a t u r e s . EVOLVEI

EVOLVEII

EVOLVEIII

Organism activities Controlled by genetics Mediated by environment Altered by organism interactions Symbiotic interactions encoded Sexual reproduction Species specificity trait included

Yes Yes Yes Yes Yes Yes

Yes Yes No No No No

Yes Yes Yes No Yes (version 1) No

Resource utilization Multiple food types Variable energy-mass ratio Mass decomposition represented

Yes No Yes

No No Yes

Yes Yes Yes

Enviromental features Spatial Time varying attributes Attribute gradients Global mass conservation Obstacles to movement

1-D Yes Discrete Yes No

None Yes Continuous Yes No

2-D Yes Continuous Yes Yes

Performance measures Organism-environment Organism-organism

Matching Local competition

Matching Global competition

Matching Pairwise interactions

Representation of time

Discrete

Discrete

Continuous

254 TABLE 2 Impression of dynamic propertiesa EVOLVE

I

EVOLVE

11

EVOLVE

III

Ecosystem level

Sensitive to initial conditions Converges to equilibrium Diversity Resilience Persistence

Untested Seldom High Medium High

No Always Low High High

Yes Seldom High Low Low

Organism level Tolerance Plasticity Diversity Complexity

None Yes High High

High No Low Low

Variable No High High

High Medium Influenced by codon usage

Low Low Evolvable

High High

Genetic level

Diversity Complexity Amenability

Evolvable (gene

structure dependent)

•Rankings (low, medium, high) are relative to the EVOLVE family, not to a real system.

collections of populations. Diversity here refers to the variety of organism types, in particular as exemplified by the range of strategies employed by groups of individuals within a single population. Resilience (Holling, 1966) describes the ability of the collection of populations to return to a specific configuration after some minor environmental perturbation, while persistence is a measure of t h e population's ability to tolerate perturbations and continue to survive, perhaps in some different configuration. The EVOLVE family exhibited a variety of different behaviors based on initial conditions, with EVOLVE III being extremely sensitive to starting conditions and the size of the initial populations. In general, as the models incorporated more detailed dynamics the population counts tended less and less to converge to equilibrium. Now let us step down to the organism level. Tolerance is a measure of the individual's ability to function over a range of environmental conditions. By plasticity

we mean an individual's ability to reform its structure (an example is the trait induction capability in EVOLVE I). Tolerance and plasticity are both components of adaptability, the ability of a system to continue to function in the face of an uncertain or unknown environment (Conrad, 1983). The diversity of an organism is a measure of the range of phenotypic combinations exploited by different individuals within a population. The complexity of an organism is a measure of the number of traits, range of traits utilized, the balance among the trait values, and the cooperative effects that emerge between related traits. The organisms in EVOLVE III were inherently more realistic and more complex in their internal operations than those of EVOLVE I and II. Both EVOLVE I and EVOLVE III were open to evolutionary development at the organism level and both generated a great deal of diversity and complexity at the organism level in the course of evolution.

255 The lowest level is that of the genetic structure. Diversity here describes the range of potential values used to code the phenotypic traits, while the complexity measures the organization of the genetic information and the amount of redundancy. Finally, the amenability of the genetic system refers to its ability to absorb mutation or other genetic change and express these as gradual transformations of the phenotypic characteristics. As indicated earlier, EVOLVE II in some cases used the amenability trait, in a reverse fashion, to facilitate big jumps between two radically different aging strategies. All of these systems are in a certain sense constructed to be amenable to evolution, since it is not code that is evolving, but parameters of existing code. Computer code by and large is too sensitive to small changes, and too likely to fail completely in response to such changes, to allow for evolution. This is one of the great difficulties of creating an artificial evolutionary system on a programmable computer. An enormous amount of computational work is necessary to simulate the structure-function plasticity that allows natural biological systems to undergo such effective evolutionary self-organization (Conrad, 1985,1988). So far the EVOLVE family, and all other models constructed to date, achieve the type of structure-function plasticity required to support evolution by severely restricting the possibilities for

function change, and hence by severely restricting the potentiality for genuinely new structures and functions to emerge. 5. Experiment and interpretation Table 3 indicates the types of experiments that have been performed with each type of system. These models generate an enormous amount of data. It is necessary to study this data directly, just as it is necessary to carefully observe ecosystems in nature. As with natural systems it is useful to work in a coherent conceptual framework and to carefully delineate one's expectations about the behavior of the system. This makes it possible to construct controlled experiments that serve to clarify important issues in evolutionary theory, and that make it possible to definitively pin down the factors contributing to the behavior of the model. In general the EVOLVE systems, like their natural counterparts, are too complicated to be predictable. For the most part their behavior defies the creator's expectations, and often contradicts the conceptual framework. This is a sign of a good model. If it contradicts our expectations one of two things must obtain. Possibly we have introduced some tacit assumptions into the model that are inappropriate to nature and in need of amendment. The recognition of such tacit assumptions serves as a kind of "deconstruc-

TABLE 3 Issues analyzed.

Adapability-stability Mutation rate control Coexistence of species Degree of polymorphism Evolutionary amenability Gene structure-population dynamics Mass cycle-populationdynamics Cooperative effects

EVOLVE I

EVOLVE 11

EVOLVE III

No No No Yes No Yes Yes Yes

Yes No Yes Yes Yes Yes Yes No

Yes Yes Yes Yes No Yes Yes No

256 tive" discovery. Alternatively our framework and pattern of reasoning may be flawed in a fundamental way. The behavior of the model may serve as a bona fide counterexample to the received theory, with real counterparts in the natural system which for some reason have not yet been observed. The artificial world then serves as means of predicting and discovering new features of the natural world. Elsewhere we have described our experimental work with the EVOLVE models in detail (Conrad, 1981; Conrad and Strizich, 1985; Rizki and Conrad, 1985). Here we will focus on the adaptability-stability experiment, since EVOLVE II and EVOLVE III gave different results. We will describe the results in just enough detail to illustrate the value of a comparative approach. The adaptability-stability experiment involves two steps. In the first step two separated groups of organisms are cultured in different environments. The environments may involve a different degree of environmental stress (low light environments, for example, might be associated with less energy input) and different degrees of environmental uncertainty. After a specified amount of time the two populations are combined, and placed in one of the two environments. We view the population that dominates subsequent to this perturbation as being more stable. If we assume that excess adaptability is a cost to the organism, the group cultured in the more uncertain environment should be more adaptable. Hence the term, adaptability-stability experiment. This assumption is true in many cases, but it is not always true (Conrad, 1983). The assumption turned out not to be true in EVOLVE II in most cases. In harsh, uncertain environments short lived species had less of a chance of surviving. Long lived species dominated, and this reduced evolutionary genetic adaptability. Since this was the only form of organism adaptability allowed, it reduced adaptability in general.

In very rich, certain environments the shorter lived, more adaptable species did much better and in some extreme cases did as well or even dominated the long lived species when the two groups were both placed in this environment. The results were far more complex when the experiment was performed with EVOLVE Ill. After a short time span the species cultured in more certain, richer environments dominated those cultured in harsh, uncertain environments, as in EVOLVE I. The organisms grown in the rich, certain environment were markedly more variable than those grown in the harsh, certain environment. The latter environment evidently pruned out superfluous eccentricities, thereby reducing adaptability. This is the same thing that happened in EVOLVE If, except that the eccentricities involved m a n y more traits than aging. After a long time span, though, the situation was reversed. The organisms cultured in the variable environment tended to dominate those cultured in the certain environment. The extra time allowed the variability of the organisms to become better matched to the variability of the environment. In effect useless eccentricities were replaced by potentially useful ones. The result with E V O L V E III corresponds to experiments with laboratory microecosystems (Conrad, 1 9 8 3 ; Rizki and Conrad, 1986). Aqueous microecosystems cultured in rich, certain environments initially do better than those cultured in harsh, uncertain environments. The more rapid accumulation of biomass in the former systems allowed for the accumulation of more total adaptability (more total potentiality) and for more eccentricities. But after a longer period of time the situation was reversed, due to the fact that the biomass of the two systems becomes more equalized and the eccentricities of the system cultured in the more variable environment are more effectively matched to the repertoire of environmental situations that can occur.

257 These differences between EVOLVE II and EVOLVE III illustrate an important methodological point. The adaptability-stability behavior exhibited by EVOLVE II may occur in nature, but if it does so it would probably be in simple systems subject to unusual constraints. In more complex systems the special processes that led to the EVOLVE II results would be masked by many other factors. As our model systems become more complex they implicate more processes, and by so doing hide the less weighty processes. The same is probably true for naturally occurring ecosystems, except that the number of processes that must be summed up are much greater. Work with simple artificial worlds models can thus serve to expose processes that might be hidden in most naturally occurring ecosystems, but which might come to the fore under special circumstances. Work with complex artificial worlds models, such as EVOLVE III, is more likely to reflect the actual balance of factors in nature, and it is such models that we should look to when we want to develop effective predictive tools. EVOLVE III demonstrates that artificial worlds models can be molded into such tools, and work with the EVOLVE family shows that the artificial worlds approach provides a powerful means for peering into the conceptual structure of evolutionary theory. 6. Towards EVOLVE IV and beyond How h r can artificial worlds models of the EVOLVE type go? The answer depends in part on the research agenda. Our agenda is to generate the niche structure of an ecosystem, at least all the essential elements of a niche structure. We should see the formation of a variety of species performing the variety of functions necessary to support a naturally occurring ecosystem. This means the e m e r g e n c e of plant like organisms, herbivores, carnivores, detritic organisms for various stages of

decomposition, and basic types of each of these that correspond to what is found in nature. The sequence of events leading to the establishment of the niche structure should reflect ecological succession, and on a longer time scale evolutionary succession. On a longer time scale our goal would be to obtain species that utilize new hierarchical levels of organization (such as multicellular levels of organization, or differentiated organ structure). Emergence here means existing structures assume new functions, or existing functions combine to yield new structures. Perhaps the reiteration of this emergence-generating interaction between structure and function is the key to the creative power of the evolution process in nature. Our experience with EVOLVE I - - I I I has suggested to us that the key to emergence is 3-D representation, at all levels of organization. In real biological systems the function of a protein is implicit in its threedimensional shape. This shape depends on a dynamic interaction among a collection of amino acids. Similarly the cell and the organism emerge from the dynamic interaction of many constituent elements. Obviously the function of an organism in an ecosystem context depends on its dynamics and structure in a complicated way. But under no circumstance can the threedimensional structure of the organism be ignored if the goal is to understand how the interactions among organisms and with the environment yields a niche structure. The geometry of plant life obviously constrains the flow of light energy and provides the environmental morphology for organisms at all trophic levels. Might it not be possible to represent the essential aspects of these structure-function relations in a purely linear world, such as EVOLVE I? The functions performed by the linear structures of EVOLVE I in fact did change in entirely unanticipated ways. This gave rise to new strings, allowing for new ways to use the existing routines; but it

258

did not affect the structure of these routines. If we had attempted this the evolution certainly would have been trapped in a deep crevice, due to the fragility of the code. In order to avoid this it is necessary to introduce some dynamic interaction among the elements of the "code", analogous to the interactions among the amino acids in a protein. This means at least a two-dimensional system. Dynamic interaction and dimensionality two or greater is necessary to achieve the gradualism necessary for emergence and evolution to occur. It might of course be possible to approach the niche structure problem in a less fundamental way. We could supply the organisms with the requisite geometry and attempt to guess mutual interactions among the organisms that would lead to the desired niche structure. This approach could lead to significant insights into evolutionary biology. But to create a bona fide artificial evolutionary system it will certainly be necessary to address the issue of emergence. The niche structure problem calls for complex, realistic models. We have seen that simple models can often lead to useful insights. To obtain a balanced concept of biological evolution, though, it is necessary to make models that can be compared to the actual phenomena. The EVOLVE family is already on the complex side. In its own time each model stressed the computer resources available to us. Recent developments in computer technology make it possible to extend the complexity and realism of these models by two orders of magnitude at least. We believe that it will some day be possible to develop artificial worlds models that serve as predictive tools for ecosystem evolution.

Acknowledgement M.C. acknowledges Grant IRI-8702600 from the U.S. National Science Foundation.

References Conrad, M., 1970, Computer experiments on the evolution of coadaptation in a primitive ecosystem, Ph.D. Thesis, Stanford University, Stanford, CA. Conrad, M, 1981, Algorithmic specification as a technique for computing with informal biological models. BioSystems 13, 303-320. Conrad, M., 1983, Adaptability: The Significance of Variability from Molecule to Ecosystem (Plenum Press, New York). Conrad, M., 1985, On design principles for a molecular computer, Commun. ACM 28, 464--480. Conrad, M., 1988, The price of programmability, in: The Universal Turing Machine: A Fifty Year Sur~ vey, R. Herken (ed.) (Oxford University Press, London). Conrad, M. and Pattee, H.H., 1970, Evolution experiments with an artificial ecosystem. J. Theor. Biol. 28, 393--409. Conrad, M. and Strizich, M., 1985, EVOLVE II: a computer model of an evolving ecosystem. BioSystems 17, 245-258. Hogeweg, P. and Hesper, B., 1983, The ontogeny of t h e interaction structure in bumblebee colonies: MIRROR model. Behav. Ecol. Soeiobiol. 12, 271-283. Hong, C.S., 1966, The strategy of building models of complex ecological systems, in: Systems Analysis in Ecology, K.E.F. Watt (ed.) (Academic Press, New York). Papentin, F., 1973, A Darwinian evolutionary system. I. J. Theor. Biol. 39, 397--415. Rada, R., 1981, Evolution and gradualness. BioSystems 14, 211-218. Rizki, M.M., 1985, A discrete event model of an evolutionary ecosystem, Ph.D. Thesis, Wayne State University, Detroit, MI. Rizki, M.M. and Conrad, M., 1985, EVOLVE III: a discrete events model of an evolutionary ecosystem. BioSystems 18, 121-133. Rizki, M.M. and Conrad, M., 1986, Computing the theory of evolution. Physica D 22, 83--99.