plain how elaborating evolutionary systems might inform how you, as a leader or manager, can enact positive social change.

Relationship Between Elaborating Evolution and Social Change

Change, especially technological change, is accelerating, which in turn affects our culture, language, and organizations. For example, over the last two decades, music, video, and photography have been digitized and entire industries have been transformed. On the horizon, books, education, magazines, and newspapers are being digitized as well; so is biology. How can you channel accelerating change in technology to improve our social condition?

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To prepare for this Discussion, think about the concept and processes of elaborating evolution and how industries, societies, and cultures evolve amid change and complexity. Consider the influence this concept might have on social change.

an explanation of how elaborating evolution helps explain social change. Then, explain how elaborating evolutionary systems might inform how you, as a leader or manager, can enact positive social change. Please be detailed in your explanation.

Fichter, L. S., Pyle, E. J., & Whitmeyer, S. J. (2010a). Expanding evolutionary theory beyond Darwinism with elaborating, self-organizing, and fractionating complex evolutionary systems. Journal of Geoscience Education, 58(2), 58–64.

Fichter, L. S., Pyle, E. J., & Whitmeyer, S. J. (2010b). Strategies and rubrics for teaching chaos and complex systems theories as elaborating, self-organizing, and fractionating evolutionary systems. Journal of Geoscience Education, 58(2), 65–85.

C
Journal of Institutional Economics (2011), 7: 3, 393–423
© The JOIE Foundation 2011 doi:10.1017/S1744137411000257
First published online 4 April 2011
Evolution as computation: integrating
self-organization with generalized
Darwinism
ERIC D . BEINHOCKER∗
McKinsey Global Institute, London, UK
Abstract: Generalized Darwinism and self-organization have been positioned as
competing frameworks for explaining processes of economic and institutional
change. Proponents of each view question the ontological validity and
explanatory power of the other. This paper argues that information theory,
rooted in modern thermodynamics, offers the potential to integrate these two
perspectives in a common and rigorous framework. Both evolution and
self-organization can be generalized as computational processes that can be
applied to human social phenomena. Under this view, evolution is a process of
algorithmic search through a combinatorial design space, while self-organization
is the result of non-zero sum gains from information aggregation. Evolution
depends on the existence of self-organizing forces, and evolution acts on designs
for self-organizing structures. The framework yields insights on the role of agency
and the emergence of novelty. The paper concludes that information theory may
provide a fundamental ontological basis for economic and institutional evolution.
1. Introduction
By what processes do institutions and economies undergo spontaneous,
discontinuous change? How does novelty in such systems arise? For well over
a century, social theorists have debated two broad explanatory frameworks
for these central questions. The first can be loosely characterized as the
evolutionary framework, with historical roots in Thorstein Veblen (Hodgson,
2004), an intellectual trajectory through Nelson and Winter (1982), and a
modern incarnation in the work of ‘generalized Darwinists’ such as Hodgson and
Knudsen (2006, 2010), Aldrich et al. (2008) and Stoelhorst (2008). The second
can be loosely characterized as the self-organization framework, with historical
roots stretching back to Adam Smith, an intellectual trajectory through Friedrich
∗Email: eric.beinhocker@mckinsey.com. The author is grateful to the Santa Fe Institute, in particular to
Brian Arthur and Doyne Farmer, as well as to the participants of the ‘Do Institutions Evolve?’ workshop
hosted by the Robert Schuman Centre for Advanced Studies, European University Institute, May 2009,
in particular Sven Steinmo, Geoffrey Hodgson and David Sloan Wilson. The author also thanks three
anonymous referees for extensive constructive suggestions. All usual caveats apply.
393
394 ERIC D . BEINHOCKER
Hayek and Joseph Schumpeter, and a modern incarnation in the work of figures
such as Foster (1997, 2000), Witt (1997, 2003) and Weise (1996).
In recent years, these two frames have been viewed as in competition, with
ongoing debates about the ontological validity and explanatory power of each
stance. Geisendorf (2009) surveys and summarizes the modern debate (2009:
377).
As Geisendorf (ibid.) notes, advocates of a ‘universal Darwinism’, such as
Hodgson and Knudsen (2006), Aldrich et al. (2008) or Stoelhorst (2008),
argue that the mechanisms of variation, selection and retention are general
characteristics of open, complex systems, the economy being one among them.
Critics, like Witt, disagree and claim that evolution in economic systems is
fundamentally different from biological evolution because economic agents are
able to change deliberately (Witt, 1992, 1997, 2003). Or they claim, like Foster,
that the driving force behind economic evolution is not selection but a selforganized
‘continual, spontaneous generation of novelty’ (Foster, 2000: 326),
going back to Schumpeter’s ideas.
Geisendorf’s assessment of this debate is that self-organization is a useful
concept, but an incomplete model of institutional and economic change in
importantrespects. The theory ‘helpsto understand why there is an endogenously
generated incentive to create novelty. And it describes how novelty might
spread,’ but ‘the process of novelty generation remains unclear’ (2009: 383).
She views universal (or generalized) Darwinism as a more fully specified model,
acknowledges that care must be taken to avoid analogizing with biology, and
attributes much criticism of the theory to misinterpretation. Crucially, she
finds no fundamental ontological contradictions between the two stances. She
(Ibid.: 386) cites Klaes’s (2004) four ontological commitments shared by most
evolutionary economists: ‘that there is change, that this change is caused, that
there is a continuity in this change in the sense that it has to be explained how a
state results from the one before, and that it takes place on several, interrelated
levels’. She claims that both the generalized Darwinist and self-organization
frames rely on these shared ontological commitments.
While Geisendorf sees promise in both approaches, no basic contradictions at
an ontological level, and several points of complementarity, she does not attempt
to resolve the dispute or integrate the perspectives. This paper will undertake that
challenge by introducing a new meta-frame – information theory, and specifically
the notion that evolution is a form of computation.
Information theory and related theories of computation are well suited
to this task as they cut across both evolution and self-organization. As we
will discuss, current evolutionary theory views evolution as a computational
process – an algorithmic search through a combinatorial space of possibilities.
Likewise, theories of self-organization are rooted in thermodynamics, which
to modern physics is just another way of talking about information (and
vice versa). Concepts such as complexity, order, emergence and novelty are
Evolution as computation 395
defined via information theory (Cover and Thomas, 2008; Davies and Gregersen,
2010). One cannot speak about either evolution or self-organization without
fundamentally relating back to information.
Such an integrated explanatory framework is important to progress the
institutional and evolutionary economics agenda. Neoclassical economics has
a framework that, after a fashion, takes into account both evolution and
self-organization. From Adam Smith’s pin factory, to Marshellian partial
equilibrium, von Neumann and Morgenstern’s game theory, Arrow–Debreu
general equilibrium and Lucas’s rational expectations, neoclassical economics
has argued that economic self-interest and price signals, mediated by rational
agents, lead inexorability to self-organized optimality. And the process by
which this self-organized optimality is achieved is the pseudo-evolutionary
neoclassical account of market competition. Neoclassically inspired institutional
economics shares this integration of self-organization and evolution. For
example, transaction-cost economics (Williamson, 2000) is both a theory of
self-organization (again, spontaneous cooperation and coordination via rational
self-interest and price signals) and (pseudo) evolution via market competition.
As Kingston and Caballero (2009: 161) note: ‘the process of institutional change
envisaged [by transaction-cost economics] is an evolutionary one in which
competitive pressure weeds out inefficient forms of organization, as originally
suggested by Alchian (1950), because those who choose efficient institutions will
realize positive profits, and will therefore survive and be imitated’.
Neoclassical theory has continued to dominate economics despite decades of
evidence on its empirical failings, its lack of explanatory power, its ontological
inconsistencies and even its computational impossibility (for a survey, see
Beinhocker, 2006). There are many possible explanations for its persistence
(Colander et al., 2009), but the ability of neoclassical theory to integrate notions
of self-organized cooperation and coordination with notions of evolutionary
competition under a common analytical framework is arguably a strength. To
be credible, any alternative theory must do likewise.
This paper is an attempt to start that integration project. Section 2 reviews
the development of the idea of evolution as computation. Section 3 articulates
a synthetic account of computational evolution – one can think of it as
general Darwinism on a universal computer. Section 4 then applies this abstract
account to an economic setting, and section 5 shows how this application
might explain patterns of economic and institutional change. Section 6 looks
at self-organization from an information theory perspective and shows how it is
inextricably bound up with evolution and vice versa. Finally, section 7 argues
that if generalized Darwinism is a ‘metatheoretical framework’, as Hodgson and
Knudsen (2010: viii) claim, then information theory is a meta-metatheoretical
framework, providing an ontological grounding for both generalized Darwinism
and self-organization as logical consequences of the laws of thermodynamics.
396 ERIC D . BEINHOCKER
If we can root a theory of economic and institutional change in modern
thermodynamics, then we will have significantly sharpened Occam’s razor.
Neoclassical economics blatantly ignores and contradicts thermodynamics
(Georgescu-Roegen, 1971; Mirowski, 1989; Beinhocker, 2006). As Sir Arthur
Eddington (1927) famously put it, ‘if your theory is found to be against the
second law of thermodynamics I can give you no hope; there is nothing for it
but to collapse in deepest humiliation’.
2. Evolution as computation
In his influential 1932 paper, the geneticist Sewall Wright wrestled with
the combinatorial problem of a typical genome with 1000 genetic loci
with 10 different allelomorphs each, together yielding 101000 possible genetic
combinations – a number vastly larger than the estimated number of particles
in the universe (Wright, 1932). How does the evolutionary process explore
such a staggeringly large space of possibility? How does it find within that
staggeringly large space the almost infinitesimally small fraction of combinations
that could potentially yield coherent, functional designs for organisms? To
analyse this problem, Wright proposed a theoretical construct whereby each
point in the genetic combinatorial set is assigned a value for its ‘adaptiveness’,
as Wright described it. This could then be visualized as a two-dimensional
surface, later described as a ‘fitness landscape’ (Dennett, 1995), with peaks and
valleys reflecting the environmental fitness of particular genomic combinations.
Evolution’s job then was to search that landscape for fit genomic combinations.
Initially Wright’s paper (1932) was viewed as a modest methodological
advance, only later did it come to be appreciated as a major reconception of
what evolution is and does. By framing evolution as a process of search through
a combinatorial space of possibilities, Wright put evolution into a realm very
familiar to mathematicians and, later, computer scientists. To these researchers,
the problem of evolutionary search across a fitness landscape looked like a
form of optimization problem, where evolution was a process of search for
maxima in a dynamically changing, high-dimensional space. Mathematically,
the fitness landscape problem shared features with various kinds of multidimension
function optimization problems, and combinatorial optimization
problems (Kauffman, 1993; Flake, 1998).
These similarities were not merely coincidental, as all of these problems either
are, or have the potential to be, what mathematicians call NP-complete or NPhard
problems (Karp, 1972) – that is, the time it takes any known algorithm
to find a solution to the problem or locate a global optima rises rapidly with
the size of the problem. The classic example is the Hamilton path or travelling
salesman problem where the challenge is to find the shortest itinerary for a
salesman travelling through n cities, stopping in each city once, and beginning
and ending in the same city. A five-city tour has 12 possible solutions, a 10-city
Evolution as computation 397
tour has 181,440 possible solutions, and a 15-city tour 4.36 × 1010 solutions.
Thus Wright’s paper put evolution in the same mathematical family as these
difficult search problems.
This led to attempts to use computers to perform evolutionary searches by
algorithm, and De Jong (2006) cites Friedman (1956) and Friedberg (1959) asthe
two earliest instances of evolutionary computation. This then led to pioneering
work in the 1960s and 1970s by figures such as Rechenberg (1965), Fogel
et al. (1966) and Holland (1962, 1967, 1975), and the birth of the field of
evolutionary computation. A broad literature developed on genetic algorithms,
genetic programming, artificial life and related methods, with applications
ranging from communications routing, to circuit design, drug design, stock
picking, machine learning and artificial intelligence (for examples, see Koza,
1992; Levy, 1992; Whitley, 1993; Mitchell, 1996; De Jong, 2006).
Within this literature, Darwinian approaches to evolution were simulated
and analysed, but viewed merely as one branch of a family tree of possible
search algorithms that also included simulated annealing, various hill-climbing
approaches and a wide variety of genetic algorithms. These can be considered
mathematical ‘cousins’ to Darwinian evolution. For example, simulated
annealing (Stolarz, 1992) is inspired by the techniques of controlled heating and
cooling used by metallurgists to strengthen metals (samurai swords were famous
for their use of this technique). The problem is to look for a stable energy state
(an energy minimum) in a large combinatorial space of possible energy states.
Imagine a ball rolling on a table full of pockets and depressions of varying depth
corresponding to energy states. Raising the temperature corresponds to shaking
the table, randomizing the location of the ball – violent shaking will keep the ball
out of the pockets, flying around the surface in a high-energy state. Cooling, or
slowing the shaking, enables the ball to settle into a pocket, though if it is shallow
it may get bounced out again. If one cools too fast, e.g., just stops shaking, the
ball may get stuck in a locally high energy state. Thus the challenge is to devise
a cooling schedule that maximizes the chances of the system finding a stable
low-energy minimum. While different from Darwinian variation, selection and
retention, simulated annealing in its mathematical characteristics shares many
common features.
Initially the field was concerned with developing algorithms and programming
techniques ‘inspired’ by biological evolution for the purposes of finding good
solutions to difficult search and optimization problems. But another branch
of the field began to consider the possibility that if evolution was able to be
simulated on computers, then, in a deep sense, following the work of Alan
Turing (1936), evolution itself was a form of computation. Holland’s (1975)
book provided a formal framework for generalizing a computational view of
evolution across both natural and artificial systems.
In the 1980s and 1990s the computational view of evolution began to
be connected with emerging work on complex systems and self-organization
398 ERIC D . BEINHOCKER
(Kauffman, 1993), with fundamental work on dissipative thermodynamic
systems by figures such as Erwin Schrodenger ¨ (1944) and Ilya Prigogine (1967),
as well as Von Neuman’s (1966) work on self-replicating systems and cellular
automata, and the physics of information (Percus et al., 2006; Bais and Farmer,
2008). This led to a further interpretation of evolution as a bootstrapping
algorithm that uses free energy to create order in complex systems. In other
words, evolution could be viewed as both shaped by forces of self-organization,
and as a process for creating self-organization – something we will return to in
section 6.
Over the past decade, this computational perspective began to link with
mathematical work on the dynamics of evolutionary systems and the modern
neo-Darwinian synthesis, to create an abstract theoretic, computational and
analytic framework that in the 1980s and 1990s began to be applied back to
biological evolutionary systems. Landweber and Winfree (2002), Crutchfield
and Schuster (2003) and Nowak (2006) provide examples of applications of
evolution as computation in natural systems. This work has led to productive
insights on topics ranging from macroevolutionary dynamics, to speciation,
mutation, punctuated equilibrium, evolutionary drift, genome architecture, and
even attempts at predictive biology.
Computational approaches to evolution have also had some impact on
the study of socio-economic systems, primarily through the use of genetic
algorithms as a method for simulating agent behavior and strategy search.
Genetic algorithms were first applied in this context in the late 1980s by Brian
Arthur, John Holland and their collaborators with the ‘Santa Fe Artificial Stock
Market’ (Arthur, 1995; Arthur et al., 1997). Since then, genetic algorithms have
been applied in a wide number of agent-based models (e.g., Epstein, 2006;
Tesfatsion and Judd, 2006), game theory models (Lindgren and Nordahl, 1994)
and other economic applications such as data mining for finance (Bauer, 1994).
As Geisendorf (2009) notes, however, there has been some criticism of these
applications for disregarding the particularities of economic evolution.
While this work has been methodologically interesting, there has been no
general attempt to apply computational theories of evolution to theories and
ontologies of economic and institutional evolution. Searches of the main journals
publishing evolutionary economic and institutional work yielded very few hitsfor
foundational citations in the evolution as computation literature (e.g., Holland,
1975), and likewise very few hits for terms such as ‘evolution + computation’
and ‘evolution + algorithm’, and those found generally addressed the use of
computational techniques in modeling (e.g., Safarzynska and Bergh, 2010)
and not the theoretical or ontological implications.1 Frenken (2006a, 2006b)
explores the implications of evolution as computation for technology evolution
1 The journals searched were the Journal of Institutional Economics, the Journal of Evolutionary
Economics and the Journal of Economic Methodology.
Evolution as computation 399
and addresses organizational evolution, but does not attempt a broader link
to theories of economic evolution. Potts’s (2000) work on microeconomic
foundations of evolutionary economics touches on many of the themes raised by
evolution and computation, in particular the evolution of complexity, and cites
some of the literature, but he does not frame his theory in computational terms.
Nor do recent survey volumes (e.g., Hanappi and Elsner, 2008; Witt, 2008)
grapple with this perspective. Sections 3 and 4 will attempt to fill that gap.
3. Algorithmic evolutionary search and the creation of order
The evolution as computation view starts with neither biology, nor a broad
view of biology and culture. Rather, it starts with a perspective that evolution
is a form of computation. We can begin with the notion that evolutionary
processes are algorithmic processes, an idea that is by now well established
in both evolutionary and computational theory (Holland, 1975; Dennett, 1995;
Landweber and Winfree, 2002).
Evolution as algorithm
An algorithm can be defined as a process that takes some set of inputs,
manipulates those inputs in a sequence of steps according to a set of rules, and
then produces a set of outputs. A baking recipe, for example, fits this definition
(e.g., input flour, eggs, butter, sugar, baking powder; stir together well; bake
at 175◦C for 30 minutes; allow to cool; then output one cake). Dennett (1995)
uses the example of a tennis tournament where one inputs players, grinds them
through a set of rules for advancing to quarter-finals, semi-finals, etc, and then
outputs a result: the winner. But as Dennett notes, a tournament process is a
fairly generic kind of algorithm; it can be used equally well for golf, soccer or
tiddlywinks, as it can for tennis. Dennett refers to such algorithms as ‘substrateneutral’
as the algorithm can run in a variety of environments and operate on
a variety of types of inputs – what defines the algorithm is the rule-set inside
it, not the particular substrate it works on. A computer software program is an
example of a substrate-neutral algorithm.
The link to computation comes from the pioneering work of Alan Turing
(1936) who formally defined algorithms and the notion of a ‘universal computer’
(sometimes referred to as a ‘Turing machine’). In essence Turing created a general
theory of computation that does not need to run on what we conventionally
think of as a computer. While in practice it may be difficult to get Microsoft
Word to run on anything other than your laptop (and sometimes that itself is
difficult), it is not impossible – for example, in the 1980s a group of students
at MIT (Massachusetts Institute of Technology) built a digital computer out of
Tinkertoys that played tic-tac-toe, though it was the size of several refrigerators
and not very fast. It has also been shown that biological DNA (deoxyribonucleic
acid) is a substrate that computes in a literal, Turing sense. Adleman (1994)
400 ERIC D . BEINHOCKER
Figure 1. Evolution classified as an algorithm
Search algorithms
Evolutionary search
algorithms
Algorithms
Other types of
algorithms
Non-evolutionary search
algorithms
Biological evolution Human social
evolution
Physical
technologies
Social
technologies
Business
Plans
Culture?
Other
evolution
Other?
Co-evolution
is the first example of an experiment where DNA molecules were artificially
‘programmed’ to compute, solving a seven-city Hamilton path problem and
doing so extremely efficiently.
One can likewise think of biological evolution as a computational algorithmic
process that runs on the substrate of DNA and the other chemical machinery
of biological organisms, but evolution itself is a more general substrateneutral
algorithm. Indeed, much of the computational evolution literature cited
explores the computational properties of evolution abstracted from its biological
instantiation (e.g., Holland, 1975; Koza, 1992; Mitchell, 1996; Landweber and
Winfree, 2002; Crutchfield and Schuster, 2003; Nowak, 2006).
If we thus classify evolution as a member of the general-class of algorithms
that can run on any Turing machine, it then follows to ask what kind of
algorithm it is (see Figure 1). There are many kinds of algorithmic processes –
optimization algorithms, compression algorithms, error-correction algorithms
and so on. Following Wright (1932) and the subsequent literature, evolution
can be characterized as a form of search algorithm that recursively explores a
combinatorial problem space, seeking out solutions that are more fit than others
according to some notion of fitness (a concept we will return to). Evolution is
not the only form of search algorithm (e.g., matching routines for searching
databases), nor is it the only algorithm that iteratively searches combinatorial
problem spaces across a fitness surface (e.g., hill-climbing or, as previously noted,
simulated annealing algorithms). Rather we can identify it as a particular form
of search algorithm that uses the Darwinian operators of variation, selection and
retention to search a design or problem space as discussed in the next section.
Evolution as computation 401
Searching design space
What distinguishes evolutionary algorithms from other search algorithms are the
characteristics of the problem space that they search, and the method by which
they search them. Dennett (1995) characterizes evolution as an algorithm suited
for finding ‘fit designs’. A ‘design’ has a purpose, e.g., the purpose of the design
for a chair is to comfortably support a human being in a sitting position. One
can also think of a design as solving a problem, e.g., the design of an Eames chair
is a candidate solution to the problem of comfortably supporting a human in a
sitting position. As long as there is a variety of candidate designs, some designs
will inevitably be more ‘fit for purpose’ or ‘solve the problem better’ than other
designs. An Eames chair might, for example, be perceived by a user as more
comfortable and more attractive than an alternative chair design and thus more
fit for purpose and a better solution to the sitting problem. While the purpose
of human designs is then to fulfill human needs (Georgescu-Roegen, 1971),
the purpose of designs created by biological evolution is simple – to survive
and reproduce in their environment. There is a near-infinite variety of possible
designs that fulfill this purpose, ranging from a bacterium to an elephant. But, as
Dawkins (1976) points out, any biological design that did not fulfill this purpose
would by definition disappear. Another way to think of it is that a tree frog is a
candidate solution to the problem of surviving and reproducing in its particular
environment, and its very existence is ipso facto proof that it was a successful
solution to that problem at a point in time.
For any design there are variants of that design that may be better or worse
at fulfilling the design’s purpose or solving the problem. What constitutes ‘better
or worse’ is referred to as the fitness function and may contain any number of
dimensions. For example, the fitness function for the design of a chair might
include dimensions of comfort, attractiveness, cost, durability and so on, while
the fitness dimensions of a tree frog might include metabolic efficiency, hopping
distance, effectiveness of camouflage and so on. The source of the fitness function
is the environment into which the design is physically rendered. A design variant
for a tree frog might be rendered into a rainforest environment of food sources,
predators, habitats, etc that shape its fitness function. A design variant for a chair
might be rendered into an environment of people sitting on it, deciding whether
they like it or not, whether to buy it or not, whether to use it or not, and so on.
Fitness functions are dynamic and change over time as the environment changes,
and there is dynamic feedback or co-evolution between designs and the fitness
function generated by their environment.
In the computational conception of evolution it is important to conceptually
separate the design of a thing from the thing itself – what Dopfer and Potts (2004)
call the first axiom of evolutionary realism: ‘all existences are bimodal matter–
energy actualizations of ideas’. A design exists as information while a rendering
of the design exists in a physical environment. For example, the information for
the design of a chair might be captured in a blueprint and a set of instructions
402 ERIC D . BEINHOCKER
for making the chair – such encoding of design information can be referred to as
a schema (Holland, 1975, 1995; Mitchell, 1996). A chair itself is then a physical
rendering of the design encapsulated by the schema. And while all physically
rendered designs are actualizations of ideas, it does not follow that all ideas or
possible schemata are or can be actualized. The set of chair designs that can
possibly be physically rendered under the laws of physics is a subset of the set
of all possible chair designs. The set of physical instantiations of chair designs
that will ever be rendered in the lifetime of the universe is then a further subset
of that. This definition applies not just to artefacts but to other forms of design
as well. The design for a shiatsu massage can be encoded in a set of instructions
and then rendered by someone providing such a massage. We can even make this
separation between schemata and physical rendering for things that are purely
information themselves. For example, one can create a schema for a possible
computer code, but until it is run on some sort of Turing machine (which is
subject to the laws of thermodynamics) it cannot be considered to be physically
rendered.
The physical rendering of a design into an environment is sometimes referred
to as an interactor (Hull, 1988). It is the physical rendering of the design that
interacts with the environment and is subject to fitness pressures, not the design
itself (though this is not to imply that the unit of selection is the interactor itself;
units of selection tend to be modules of design within schemata). Interactors
can be composed of matter and energy (e.g., an organism in biology) or can be
information themselves (e.g., in a genetic algorithm the schema may code for a
bit string that is then subject to selection pressures – this is a physical rendering
as well because the computational operations require energy).
The process of translating from the information world of design encoded
in schemata into the physical world of interactors is an often-overlooked
aspect of evolution. It is not a feature typically highlighted in discussions
of general Darwinism, though Hodgson and Knudsen (2010: 122) include a
‘generative replicator’ in their scheme that fulfills a similar function. The process
of translating from information to reality shapes important characteristics of
the process. In order for a design to be rendered there must be a schemareader/interactor-builder
to do the rendering (for simplicity I will refer to
this concept as a reader/builder). In the biological world, for mammals the
reader/builder is a female womb, for birds, fish and amphibians it is an egg –
both render from the schema of DNA into an interactor organism. For a chair
the reader/builder might be a carpenter, for a shiatsu massage it might be a
masseuse. The need for a reader/builder has two important implications.
First, the schema does not have to capture all of the information in the design,
only enough so that the design can be reliably rendered by the reader/builder.
The design for a chair has to only be detailed enough for a qualified carpenter
with the right tools and materials to build it. The design for a mouse encoded
in mouse DNA only has to be sufficient to be rendered by a female mouse
Evolution as computation 403
womb into a baby mouse. This implies significant knowledge and design in the
reader/builder, and one can then ask where this knowledge and design comes
from. The answer of course is that reader/builders are the result of evolutionary
processes themselves. In biology, schemata code for interactors that also serve as
reader/builders (the reading and building is part of the design), giving biological
evolution its bootstrapping character. In other substrates, the reader/builder
may be the product of multiple evolutionary processes, e.g., the carpenter’s
ability to serve as a reader/builder for chairs may be the product of evolution
across biological, technological and social substrates. We will discuss the role
of re

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