Embed Size (px)
Citation preview
e c o l o g i c a l c o m p l e x i t y 3 ( 2 0 0 6 ) 3 6 1 – 3 6 8
Bounded rationality and hierarchical complexity: Two pathsfrom Simon to ecological and evolutionary economics
Tim Foxon a,b,*a 4CMR – Cambridge Centre for Climate Change Mitigation Research, Department of Land Economy, University of Cambridge, CB3 9EP, UKbCentre for Environmental Policy, Imperial College, South Kensington, London SW7 2AZ, UK
a r t i c l e i n f o
Published on line 13 March 2007
Keywords:
Bounded rationality
Hierarchical complexity
Co-evolution of technologies and
institutions
Technological transitions
a b s t r a c t
This paper examines two paths by which the work of Herbert Simon has influenced the
development of ecological and evolutionary economics: bounded rationality and hierarch-
ical complexity. It argues that there is scope for further consideration of the implications of
these ideas, particularly their inter-relation. This is illustrated through some recent ideas on
the co-evolution of technologies and institutions. This is related to understanding transi-
tions of technological systems, and how these might be steered or modulated towards
greater sustainability.
# 2007 Elsevier B.V. All rights reserved.
avai lab le at www.sc iencedi rec t .com
journal homepage: ht tp : / /www.e lsev ier .com/ locate /ecocom
1. Introduction
This paper examines two paths by which the work of Herbert
Simon has influenced the development of ecological and
evolutionary economics: bounded rationality and hierarchical
complexity, and argues that there is scope for further
consideration of the implications of these ideas, particularly
their inter-relation. This is illustrated with some recent ideas
relating to understanding transitions of technological sys-
tems, and how these might be steered or modulated towards
greater sustainability, applying ideas on the co-evolution of
technologies and institutions.
In Section 2, we examine the concept of ‘bounded
rationality’, i.e. that actors are limited in their ability both
to gather and process information relevant to decision-
making (Simon, 1955, 1959), and discuss some implications
for economic thinking. The idea that actors ‘satisfice’, rather
than optimise, by choosing an option that satisfies their
chosen criteria, forms a key plank both of the evolutionary
theory of economic change proposed by Nelson and Winter
(1982) and of approaches to understanding innovation from a
* Correspondence address: 4CMR – Cambridge Centre for Climate ChangCambridge, CB3 9EP, UK. Tel.: +44 1223 764874; fax: +44 1223 337130.
E-mail address: [email protected].
1476-945X/$ – see front matter # 2007 Elsevier B.V. All rights reservedoi:10.1016/j.ecocom.2007.02.010
systems perspective. In Section 3, we examine Simon’s (1962/
1996) analysis of multi-level or hierarchic complex systems,
i.e. systems consisting of multiple levels of inter-related
subsystems. In particular, he argued that the relative stability
of intermediate levels enables such systems to emerge more
quickly through evolutionary processes.
Section 4 looks at recent work (Unruh, 2000; Nelson and
Sampat, 2001; Geels, 2002; Foxon, 2007) examining the
problem of lock-in of current unsustainable technological
systems and argues that this arises through a process of co-
evolution of technologies and institutions. Section 5 considers
implications of this idea for a transition to more sustainable
technological systems, and argues that such a transition could
be facilitated by the promotion of relatively stable intermedi-
ate levels of more sustainable techno-institutional systems,
for example, through the promotion of niches in which radical
innovation can occur. Section 6 relates this to some current
ideas on policy processes for sustainable innovation, and
Section 7 provides conclusions and ideas for further research.
The paper aims to promote consilience (Wilson, 1998)
between ideas from complex systems theory, ecological
e Mitigation Research, Department of Land Economy, University of
d.
e c o l o g i c a l c o m p l e x i t y 3 ( 2 0 0 6 ) 3 6 1 – 3 6 8362
economics, evolutionary economics and innovation systems
theory, and to suggest ways in which this could be taken
forward. Despite having developed largely independently,
ecological and evolutionary economics share some common
heritage. This paper highlights how the ideas of Simon form
part of this common heritage and suggests how they could
contribute to a reconciliation of the ‘two long lost siblings’ of
ecological and evolutionary economics.
2. Bounded rationality
An important feature which distinguishes heteorodox eco-
nomic approaches, including ecological and evolutionary
economics, from mainstream ‘neo-classical’ economics is
the differing conceptions of rationality that they employ. The
neo-classical approach is underpinned by the concept of
‘rational economic man’ who has perfect knowledge of all
possible choices at any particular time, together with
unlimited power to compute the utility implications of these
choices. The recent extension of this conception to include
‘rational expectations’, the ability to work out and take into
account all future consequences of current choices, adds to
the conceptual weight born by this axiom. The assumption of
perfectly rational individual behaviour enables neo-classical
economics to make clear predictions about the behaviour of
economic systems, especially in the short-term, but provides a
poor conceptual basis for understanding long-term change of
socio-economic systems, including their interactions with
ecological systems.
Simon (1955, 1986) sought to provide a more realistic and
useful conception that would ‘‘replace the global rationality of
economic man with a kind of rational behaviour that is
compatible with the access to information and the computa-
tional capacities that are actually possessed by organisms,
including man, in the kinds of environments in which such
organisms exist’’ (Simon, 1955, p. 99). This raises the problem
of how to characterise the behaviour of actors exhibiting such
bounded rationality. His proposed solution is that actors
‘satisfice’ rather than optimise, i.e. they search for a
satisfactory choice, given the information available and their
ability to compute the consequences. In answer to the
objection that rational actors would seek to gather more
relevant information to provide more accurate calculation of
the likely consequences, he pointed out that such information
gathering is costly, and hence there is always a trade-off
between allocating time and resources to gathering further
information and proceeding to act on the basis of current
information. A similar trade-off applies to investment of time
and resources allocated to enhancing computational capa-
cities, for example, through education or training. This idea
that individuals satisfice with respect to desired levels of
aspiration is supported by a range of evidence from experi-
mental economics (e.g. Tversky and Kahneman, 1986).
Simon (1959) went on to argue that similar considerations
apply to decision-making by firms. Rather than assuming that
rational economic firms act to maximise profits, he argued that
firms will satisfice by seeking an attainable level or rate of profit,
orother goal, such as a reasonable market share or level of sales.
A behavioural theory of the firm, starting from this assumption,
was developed by Cyert and March (1963). The standard neo-
classical response to this position (Friedman, 1953) is that firms
in competitive markets must act ‘as if’ they maximise profits,
since economic ‘natural selection’ between firms will ensure
that the firms which are actually maximising profits are the
ones most likely to survive. The flaw in this response, pointed
out by Winter (1964) and Hodgson (1999), is that it assumes that
evolutionary change will automatically lead to an optimal
solution, in this case profit maximisation, by weeding out the
non-optimal alternatives. In fact, evolutionary change only
leads to local maxima in the fitness landscape, in this case
corresponding to satisficing solutions, rather than a single
global maximum, i.e. an optimal solution. One way to see this is
to note that in a complex, changing environment of other firms,
technologies and preferences, firms are highly unlikely to have
the information or computational power to discover or
maintain the optimal profit maximising solution.
The appropriate way of characterising bounded rationality
has been widely debated in the economics literature. Coase
(1937) argued that the reason individuals organise themselves
together into firms is in order to minimise transaction costs.
This form of bounded rationality was incorporated into further
theoretical developments on the nature of the firm by
Williamson (1975, 1985), which were seen as consistent with
the neo-classical economic principle that argues actors
minimise their marginal costs. However, in later papers,
Simon (1978a,b, 1986) made clear that he favoured a more
radical interpretation in terms of procedural, rather than
substantive, rationality. This sees bounded rationality as the
process of finding reasonable solutions given necessarily
limited information and computational capacities. Unlike the
neo-classical view, this interpretation emphasises the dis-
tinction between the real world and the actor’s perception of,
and reasoning about, the world. This implies that actors’
expectations of future states, and the social processes by
which these are created, are crucial to understanding their
decision-making (cf. MacKenzie, 1992).
A sophisticated evolutionary theory of economic change
was subsequently developed by Nelson and Winter (1982),
building on the foundation of Simon’s view of bounded
rationality. Their key concept is that of ‘routine’, a regular and
predictable pattern of behaviour undertaken by a firm, such as
a specific production activity, hiring procedure, ordering
process or R&D activity. Routines are seen as the analogues
of genes in biological evolutionary theory. They are persistent
features of firms that, together with environmental condi-
tions, determine their behaviour, are heritable (contingent
structures passed on over time), and selectable, i.e. firms
following certain routines will do better than firms following
other routines, resulting in increasing frequency of successful
routines in the population over time. The routines employed
by a firm at any particular time are those that ‘satisfice’
according to its chosen criteria. When a particular routine is
no longer deemed to be satisfactory, for example, because of
changing market conditions, this triggers a search for a new
routine, for example, through increased investment in R&D.
Nelson and Winter developed detailed models of firm
behaviour based on this approach, and used these to model
processes of economic change, including technological
change and economic growth.
e c o l o g i c a l c o m p l e x i t y 3 ( 2 0 0 6 ) 3 6 1 – 3 6 8 363
We return to the consideration of evolutionary economic
approaches in Section 4, but next turn to Simon’s discussion of
hierarchical complexity.
3. Hierarchical complexity
Though complexity theory is often associated with the
exciting developments over the last 20 years at places like
the Santa Fe Institute, many of the key ideas were fore-
shadowed in the development of ‘general systems theory’ in
the 1950s and 1960s by von Bertalamffy (1968), Boulding (1950)
and others (see Hammond, 2003). A further influential article
on ‘the architecture of complexity’ by Herbert Simon,
originally published in 1962, was one of the first to explicitly
link systems theory to complexity (Simon, 1962). This paper
argued that ‘‘complexity frequently takes the form of
hierarchy and that hierarchic systems have some common
properties independent of their specific content’’ (Simon, 1996,
p. 184).
Simon did not attempt a formal definition of complexity,
which is still elusive, but pragmatically defined a complex
system as one made up of a large number of parts that have
many interactions. He further defined a hierarchic system as
one composed of multiple levels of inter-related subsystems.
Note that this is only taken to imply a structural relationship
and not necessarily a formal organisational hierarchy, in
which subordinate subsystems each report to a ‘boss’ at a
higher level. He then went on to examine the timescale of
evolution of complex systems and to argue that hierarchic
systems will evolve more quickly than non-hierarchic systems
of comparable size.
This is illustrated with a parable of two watchmakers, Hora
and Tempus. Each makes watches composed of 1000 parts
each, but while those of Tempus have to be assembled in one
whole, Hora’s are made up of three levels of subassemblies of
10 elements each. So, Tempus has to make a complete
assembly in one go, and it is assumed that if he is interrupted,
the partially completed assembly will fall apart. Hence, for
each interruption, he will lose more work, and he will take
many more attempts to produce a complete assembly. Hora,
on the other hand, has to complete 111 subassemblies for each
complete watch, but she will lose less work for each
interruption and will take far fewer attempts to make a
complete assembly. If the probability of interruption is about 1
in 100, then Tempus will take around 4000 times as long as
Hora to assemble a complete watch.
Simon argued that the same principle of faster evolution of
a complex structure consisting of relatively stable sub-
structures will apply to any biological or social system and
so such hierarchic systems are likely to be much more
common than non-hierarchic complex systems. For example,
a problem-solving process, such as safe cracking, consisting of
selective trial and error, in which partially successful
approaches are retained, will find a solution much more
rapidly than a completely random trial and error process.
Furthermore, many hierarchies form nearly decomposable
systems, in which the interactions between subsystems are
weak but not negligible compared to those within subsystems.
In this case, the short-run behaviour of each component
subsystem may be analysed as approximately independent of
the short-run behaviour of the other components, and the
long-run behaviour similarly is seen to only depend in an
aggregate way on the behaviour of the other components.
Thus, hierarchic complex systems are argued both to be
more common and to be more comprehensible, often in terms
of a process description of the dynamics of how they are
constructed rather than a state description of their final
configuration. Very similar ideas on the role and evolution of
hierarchical complex structures have been explored within
the realm of ecological complexity. For example, the structure
and evolution of food webs, complex networks of feeding
(trophic) interactions among diverse species in communities
or ecosystems has been investigated by Dunne (2006) and
Martinez (2006).
In Section 5, we examine the implications of hierarchical
complexity for transitions to more sustainable technological
systems. In the next section, we examine further the co-
evolution of technological and institutional systems.
4. Co-evolution of technologies andinstitutions
Both ecological and evolutionary economics are interested in
how technological systems change over time, and how
changes in social systems interact with those in technological
systems. This is driven by concerns over human impacts on
natural environmental systems, including human-induced
climate change, as the impacts of demands for goods and
service are strongly mediated by the socio-technical systems
in which they are embedded. Perspectives which emphasise
co-evolutionary processes of change between technological
and social systems have been pursued in both ecological and
evolutionary economics and could provide a bridge between
these two approaches.
An important conception of changes in social and
environmental systems as a process of co-evolution whereby
‘‘cultures affect which environmental features prove fit and
environments affect which cultural features prove fit’’ was
provided from an ecological economics perspective by
Norgaard (1994). He argued that the challenge of sustaining
environmental systems whilst enabling socio-economic
development for the majority world requires a radical
reconceptualisation away from linear and individualistic
thinking to a more systemic, participatory and process-
oriented view. His co-evolutionary approach explores the
interactions between values, knowledge, organisation, envir-
onment and technology. These ideas were taken up by Norton
et al. (1998) in their critique of the neo-classical economic view
of preference formation. They argued that both ecological and
economic processes are characterised by positive feedbacks,
self-reinforcement and autocatalysis, giving rise to increasing
returns, lock-in, path dependence, multiple equilibria and
sub-optimal efficiency.
A strong co-evolutionary line of thinking has also devel-
oped within evolutionary economics, strongly influenced by
ideas from complex systems theory. In his work on competi-
tion between technologies at the Santa Fe Institute, Brian
Arthur formulated the idea of technological ‘lock-in’. Whilst
e c o l o g i c a l c o m p l e x i t y 3 ( 2 0 0 6 ) 3 6 1 – 3 6 8364
mainstream economics focussed on cases of constant or
decreasing returns, Arthur (1989, 1994) argued that the
adoption of technologies displays increasing returns, i.e. the
more a technology is adopted, the more likely it is to be further
adopted. He showed that this is an example of positive
feedback, well known in other complex systems contexts
(Arthur, 1988, 1997). He identified four major classes of
increasing returns: scale economies, learning effects, adaptive
expectations and network economies, which contribute to this
positive feedback in favour of existing technologies. The first of
these, scale economies, occurs when unit costs decline with
increasing output. For example, when a technology has large
set-up or fixed costs because of indivisibilities, unit production
costs decline as they are spread over increasing production
volume. Thus, an existing technology often has significant
‘sunk costs’ from earlier investments, and so, if these are still
yielding benefits, incentives to invest in alternative technolo-
gies to garner these benefits will be diminished. Learning effects
act to improve products or reduce their cost as specialised skills
and knowledge accumulate through production and market
experience. This idea was first formulated as ‘learning-by-
doing’ (Arrow, 1962), and learning curves have been empirically
demonstrated for a number of technologies, showing unit costs
declining with cumulative production (IEA, 2000). Adaptive
expectations arise as increasing adoption reduces uncertainty
and both users and producers become increasingly confident
about quality, performance and longevity of the current
technology. This means that there may a lack of ‘market pull’
for alternatives. Network or co-ordination effects occur when
advantages accrue to agents adopting the same technologies as
others (see also Katz and Shapiro, 1985). This effect is clear, for
example, in telecommunications technologies, e.g. the more
others that have a mobile phone or fax machine, the more it is in
your advantage to have one (which is compatible). Similarly,
infrastructures develop based on the attributes of existing
technologies, creating a barrier to the adoption of alternative
technologies with different attributes.
In a simple model of two competing technologies, these
increasing returns can amplify small, essentially random,
initial variations in market share, resulting in one technology
achieving complete market dominance at the expense of the
other—referred to as technological ‘lock-in’ (Arthur, 1989). He
argued that, once lock-in is achieved, this can prevent the take
up of potentially superior alternatives. That this lock-in occurs
in practice was illustrated in a number of historical case
studies, including the QWERTY keyboard (David, 1985) and
‘light water’ nuclear reactors (Cowan, 1990).
This idea may also be applied at the level of technological
systems. The systems approach emphasises that individual
technologies are not only supported by the wider technolo-
gical system of which they are part, but also by the
institutional framework of social rules and conventions that
reinforces the technological system. North (1990) argued that
all the features identified by Arthur as creating increasing
returns to adoption of technologies can also be applied to
institutions. Pierson (2000) argued that political institutions,
such as laws and regulations, are particularly prone to
increasing returns. For example, actors with political power
under the current institutional rule-system will act to prevent
changes to those rules that would diminish their power.
In his seminal book on institutions, institutional change
and economic performance, North (1990) explicitly acknowl-
edges the contribution of Simon’s view of bounded rationality
in decision-making to his approach. He also recognises the
importance of Nelson and Winter (1982) evolutionary theory of
economic change, which, as we have seen, draws heavily on
Simon’s ideas. In recent work, Nelson has developed further
an appreciative theory of technological systems change
arising through the co-evolution of technologies, institutions
and organisations (Nelson and Sampat, 2001; Nelson, 2002,
2005). He argues that this is the fundamental process under-
lying the growth of economies.
In parallel to Nelson’s co-evolutionary theory, other
approaches within ecological economics have explored ideas
relating to the co-evolution of technologies and institutions.
Boulding (1966) was one of the first to apply evolutionary
economic thinking to the global ecological crisis. In his co-
evolutionary revisioning, Norgaard (1994) argued for the
importance of institutional change, for example, to improve
democratic involvement in the process of knowledge creation.
An ecological economic framework for the analysis of
institutional change based on the co-evolution of economic
behaviour and institutions has been developed by van den
Bergh and Stagl (2003).
Unruh (2000, 2002) argued the co-evolution of technologies
and institutions at a systems level can lead to ‘lock-in’ of
techno-institutional systems,asa result of thebenefits accruing
through the process of increasing returns. He argued that high
carbon, fossil-fuel-based energy systems in industrialised
countries have undergone a process of technological and
institutional co-evolution, leading to the current state of carbon
lock-in. The positive feedbacks of increasing returns both to the
high carbon technologies and to their supporting institutions,
including rules, ways of thinking and incentives, created rapid
expansion in the development of this technological system, so
that it now incorporates massive technological infrastructures
and a small number of powerful actors, e.g. producing nations
and large firms. The actors and elements of this system strongly
discourage radical changes which would fundamentally alter
the system. The development of this fossil-fuel-based energy
system and associated abundant supplies of cheap energy to
industrialised countries has led to rapid improvements in
material affluence. However, increasing concerns over the
severity of human-induced climate change have led some
governments, including the UK and Germany, to commit
themselves to a transition to a sustainable, low carbon energy
system, based on renewable forms of energy and much greater
energy efficiency, over the first half of this century (DTI, 2003,
2006). This will require finding ways to overcome the current
state of carbon lock-in (Foxon, 2003,2007).
In the next section, we aim to show how Simon’s ideas on
bounded rationality and hierarchical complexity are relevant
to understanding and promoting such a transition.
5. Transition to more sustainabletechnological systems
An important contribution to understanding how technolo-
gical systems change has come from recent developments in
e c o l o g i c a l c o m p l e x i t y 3 ( 2 0 0 6 ) 3 6 1 – 3 6 8 365
innovation systems theory, examining the dynamic, cumulative,
systemic and uncertainnature of technological change (Freeman
and Soete, 1997; Grubler, 1998; Grubler et al., 2002). This
innovation systems approach emphasises the systemic nature
of innovation processes, involving multiple actors and inter-
actions; the importance of uncertainty in relation to decision-
making processes of actors; and the role of institutional
factors as drivers of, and barriers to, innovation (Foxon, 2003,
2004). Hence, these approaches implicitly incorporate Simon’s
view of bounded rationality, though usually without explicit
acknowledgement.
In this approach, an innovation system is defined as ‘‘the
elements and relationships which interact in the production,
diffusion and use of new, and economically-useful, knowl-
edge’’ (Lundvall, 1992). Early work in this approach focussed
on national systems of innovation, following a pioneering
study of the then-successful Japanese economy by Freeman
(1988). In a major multi-country study, Nelson (1993) and
collaborators compared the national innovation systems of 15
countries, finding that the differences in successful innova-
tion between them reflected different institutional arrange-
ments, including: systems of university research and training
and industrial R&D; financial institutions; management skills;
public infrastructure; and national monetary, fiscal and trade
policies.
Within the innovation systems approach, a framework for
understanding how the existing technological and institu-
tional system constrains the evolution of technologies is
provided by three-level framework of Kemp (1994), consisting
of technological niches, socio-technical regimes and landscapes. This
posits that each higher level has a greater degree of stability
and resistance to change than the level below, due to
interactions and linkages between the elements forming that
configuration. The central level of socio-technical regime
represents the prevailing set of technologies and institutions
and their interactions, i.e. ‘‘the rule-set or grammar embedded
in a complex of engineering practices; production process
technologies; product characteristics, skills and procedures;
all of them embedded in institutions and infrastructures’’ (Rip
and Kemp, 1998). The higher landscape level represents the
broader political, social and cultural values and institutions
that form the deep structural relationships of a society.
Whereas the existing regime generates incremental innova-
tion, radical innovations are generated in niches, the lower
level. As a regime will usually not be homogeneous, niches
occur, providing spaces that are at least partially insulated
from ‘normal’ market selection in the regime, for example,
specialised sectors of the market, or locations where a slightly
different rule-set applies. Niches provide locations for learn-
ing processes to occur, and space to build up the social
networks that support innovations, such as supply chains and
user–producer relationships.
This three-level framework was used by Geels (2002, 2005)
to examine a number of historical transitions between
technological systems, such as from that of sailing ships to
steamships. He argued that novelties typically arise in niches,
which are embedded in, but partially isolated from existing
regimes and landscapes. For example, transatlantic passenger
transport formed a key niche for the new steamship system. If
these niches grow successfully, and their development is
reinforced by changes happening more slowly at the regime
level, then it is possible that a regime shift will occur. Geels
argues that regime shifts, and ultimately transitions to new
socio-technical landscapes, may occur is through a process of
niche-cumulation. This means that a number of initially
separate niches for the new technological system are created,
and these gradually grow and come together to form a new
regime. This is reminiscent of a systems phase transition, for
example that from water to steam, in that incremental
changes at a lower system level give rise to a change of state
at a higher level.
As Simon argued, complex systems which are structured in
a hierarchic way will evolve faster than similar systems with
non-hierarchic structure. This provides another way of
looking at the above picture of technological transitions.
The creation of stable intermediate levels in the form of niches
or groups of niches makes the transition happen faster and
makes for a transition process that is better able to cope with
set-backs or reactions from the existing regime. The latter are
often know as the sailing ship effect after the way that, in this
case, the existing sailing ship regime was stimulated to
undergo a faster rate of incremental innovation in response to
the competition from the emerging steamship regime.
This suggests that thinking about how transitions in
technological systems occur and how they could be steered
in the direction of greater environmental sustainability would
benefit from greater appreciation and application of the ideas
of hierarchical complexity, which were explored originally by
Simon and have been developed further within the realms of
ecological complexity and ecological economics.
6. Policy implications
The above picture suggests that the design of regulatory or
fiscal incentives to overcome the current carbon lock-in and
promote a transition to a sustainable low carbon economy
should take into account the need to create stable inter-
mediate levels in the technological transition process.
A useful step towards implementing this suggestion was
the idea of ‘strategic niche management’ put forward by Kemp
et al. (1998), in the context of the three-level model. This
proposed the idea of promoting shifts to more sustainable
regimes through the deliberate creation and support of niches.
Our suggestion would go beyond this by arguing that policy
measures should support the creation of stable intermediate
states for more sustainable technological systems, not just in
the form of niches, but also of groups of niches or other states
intermediate between these and a fully specified new regime.
The idea of strategic niche management has since been
incorporated by Rotmans, Kemp and colleagues into a broader
concept of transition management (Kemp and Rotmans, 2005).
This combines the formation of a vision and strategic goals for
the long-term development of a technology area, with
transition paths towards these goals, and steps forward,
termed experiments, that seek to develop and grow niches for
more sustainable technological alternatives. The transition
approach was adopted in the Fourth Netherlands Environ-
mental Policy Plan, and is now finding practical application,
supporting innovation in energy policy, where the Dutch
e c o l o g i c a l c o m p l e x i t y 3 ( 2 0 0 6 ) 3 6 1 – 3 6 8366
government is working along with industrial and local
stakeholders (Ministry of Economic Affairs, 2004).
Complexity theory was used by van der Brugge and
Loorbach (2005) to analyse this type of transition dynamics
in terms of shifts between domains of attraction, in a
presentation at the Liverpool conference from which the
papers in this special issue are drawn. They apply the ideas of
complex adaptive systems, in which interactions between the
elements in a system give rise to stable domains of attractors
bounded by thresholds, and co-evolutionary interaction
patterns may lead to irreversible pathways. We think that
their approach is complementary to that presented here.
Related ideas for transforming policy processes to promote
sustainable innovation were presented in a report for policy-
makers by the author and colleagues (Foxon et al., 2004, 2005),
arising out of stakeholder workshops and case studies on low
carbon innovation undertaken for a project under the ESRC
Sustainable Technologies Programme (STP, 2002–2006). This
report elaborated five guiding principles to inform strategic
thinking about the policy goals, processes, measures and
instruments appropriate for a Sustainable Innovation (SI) policy
regime:
(1) S
timulate the development of a sustainable innovation policyregime that brings together appropriate strands of current
innovation and environmental policy and regulatory
regimes;
(2) A
pply systems thinking and practice, engaging with thecomplexity and systemic interactions of innovation sys-
tems and policy-making processes;
(3) A
dvance the procedural and institutional basis for the deliveryof sustainable innovation policy;
(4) D
evelop an integrated mix of policy processes, measures andinstruments that cohere to promote sustainable innovation;
(5) I
ncorporate policy learning as an integral part of sustainableinnovation policy process.
7. Conclusions and further research
This paper has examined two key ideas articulated by Herbert
Simon: bounded rationality and hierarchical complexity. We
have argued that these ideas form part of the common
heritage of ecological and evolutionary economics, and that
further development and inter-relation of these ideas,
particularly in the context of ecological complexity, could
lead to a fruitful reconciliation between these approaches.
This is demonstrated through the application of these ideas to
understanding transitions of technological systems, and how
these might be steered or modulated towards greater
sustainability. This draws on ideas of processes of co-
evolution of technological and social systems.
The paper represents mostly ideas for further research
rather than polished findings, but it suggests ways in which
relations between ideas from complexity theory, ecological
economics, evolutionary economics and innovation systems
theory could usefully be further explored. A particular
challenge is how to relate the behaviour of actors, i.e.
individuals or firms, exhibiting bounded rationality within a
system to the dynamics of system change at a higher level.
Alternative approaches to the conventional utility-maximis-
ing individual of neo-classical economics have been pursued
within ecological economics in the context of environmental
policy and sustainability (e.g. van den Bergh et al., 2000; Faber
et al., 2002). Further work examining the co-evolution of
technologies and institutions could seek to relate ‘satisficing’
choices by actors to higher level system dynamics. This could
draw on the rich seam of work on co-evolutionary processes
that has developed within ecological and evolutionary
economics.
Complex systems theory has developed greatly over the
last 20 years, and been applied to a range of domains from
ecological and social systems. Investigating system properties,
such as robustness under perturbations and adaptability to
changing environmental conditions, provides an important
means of understanding these systems and their dynamics
(Forrest et al., 2005; Berkhout and Gouldson, 2005). We argue
that further use of evolutionary thinking, together with the
insights into bounded rationality and hierarchical complexity
developed by a previous generation of researchers such as
Simon, could fruitfully be incorporated into this research.
Acknowledgements
This article is based on a paper presented in the Ecological
Economics Session, Complexity, Science and Society Conference,
University of Liverpool, 11–14 September 2005. The author
would like to thank Kate Farrell and Ralph Winkler, the
organisers of that session and editors of this special issue, for
their help and support, and, in particular, for suggesting the
image of ecological and evolutionary economics as ‘long lost
siblings’. He would also like to thank two anonymous referees
for their useful comments on an earlier draft of the paper.
r e f e r e n c e s
Arrow, K., 1962. The economic implications of learning bydoing. Rev. Econ. Stud. 29, 155–173.
Arthur, W.B., 1988. Self-reinforcing mechanisms in economics.In: Anderson, P.W., Arrow, K.J., Pines, D. (Eds.), TheEconomy as an Evolving Complex System, SFI Studies in theSciences of Complexity. Addison Wesley, Reading, MA.
Arthur, W.B., 1989. Competing technologies, increasing returns,and lock-in by historical events. Econ. J. 99, 116–131.
Arthur, W.B., 1994. Increasing Returns and Path Dependence inthe Economy. University of Michigan Press.
Arthur, W.B., 1997. In: Arthur, W.B., Durlauf, S., Lane, D. (Eds.),‘Process and Emergence in the Economy’, Introduction toThe Economy as an Evolving Complex System II, SFI Studiesin the Sciences of Complexity. Addison Wesley, Reading,MA.
Berkhout, F., Gouldson, A., 2005. Inducing, shaping, modulating:perspectives on technology and environmental policy. In:Berkhout, F., Leach, M., Scoones, I. (Eds.), NegotiatingEnvironmental Change: New Perspectives from SocialScience. Edward Elgar, Cheltenham.
Boulding, K., 1950. A Reconstruction of Economics. Wiley, NewYork.
e c o l o g i c a l c o m p l e x i t y 3 ( 2 0 0 6 ) 3 6 1 – 3 6 8 367
Boulding, K., 1966. The economics of coming spaceship earth.In: Daly, H. (Ed.), Towards a Steady State Economy. WHFreeman and Co., San Francisco.
Coase, R., 1937. The nature of the firm. Economica 4,386–405.
Cowan, R., 1990. Nuclear power reactors: a study intechnological lock-in. J. Econ. Hist. 50, 801–814.
Cyert, R., March, J., 1963. A Behavioral Theory of the Firm.Prentice Hall, Englewood Cliffs, NJ.
David, P., 1985. Clio and the economics of QWERTY. Am. Econ.Rev. 75, 332–337.
Department of Trade and Industry (DTI), 2003. Energy WhitePaper: Our Energy Future—Creating a Low Carbon Economy,TSO (The Stationery Office), London.
Department of Trade and Industry (DTI), 2006. The EnergyChallenge: Energy Review Report 2006, TSO (The StationeryOffice), London, available at http://www.dti.gov.uk/energy/review/index.html.
Dunne, J.A., 2006. The network structure of food webs. In:Pascual, M., Dunne, J.A. (Eds.), Ecological Networks: LinkingStructure to Dynamics in Food Webs. Santa Fe InstituteStudies on the Sciences of Complexity, Oxford UniversityPress.
Faber, M., Petersen, T., Schiller, J., 2002. Homo oeconomicus andhomo politicus in Ecological Economics. Ecol. Econ. 40, 323–333.
Forrest, S., Balthrop, J., Glickman, M., Ackley, D., 2005.Computation in the wild. In: Park, K., Willins, W. (Eds.), TheInternet as a Large-scale Complex System. OxfordUniversity Press.
Foxon, T.J., 2003. Inducing Innovation for a Low-carbon Future:Drivers, Barriers and Policies. The Carbon Trust, Londonalso available at http://www.thecarbontrust.co.uk/Publications/publicationdetail.htm?productid=CT-2003-07.
Foxon, T.J., 2004. Innovation systems as the setting for the co-evolution of technologies and institutions. In: PaperPresented at ‘Organisations, Innovation and Complexity:New Perspectives on the Knowledge Economy’ Conference,University of Manchester, September 9–10.
Foxon, T.J., 2007. Technological lock-in and the role ofinnovation. In: Atkinson, G., Dietz, S., Neumayer, E. (Eds.),Handbook of Sustainable Development. Edward Elgar,Cheltenham.
Foxon, T.J., Makuch, Z., Mata, M., Pearson, P., 2004. Informingpolicy processes that promote sustainable innovation: ananalytical framework and empirical methodology, STPWorking Paper 2004/4, available at http://www.sustainabletechnologies.ac.uk/PDF/Working%20papers/106b.pdf.
Foxon, T.J., Pearson, P., Makuch, Z., Mata, M., 2005.Transforming policy processes to promote sustainableinnovation: some guiding principles, Report for policy-makers, ESRC Sustainable Technologies Programme, ISBN 1903144 02 7, Imperial College London, March 2005, availableat http://www.sustainabletechnologies.ac.uk/PDF/project%20reports/SI_policy_guidance_final_version.pdf.
Freeman, C., 1988. Japan: a new national system of innovation?In: Dosi, G., Freeman, C., Nelson, R., Silverberg, G., Soete, L.(Eds.), Technical Change and Economic Theory. PinterPublishers, London, pp. 330–348.
Freeman, C., Soete, L., 1997. The Economics of IndustrialInnovation, third ed. Pinter, London.
Friedman, M., 1953. The methodology of positive economics. In:Essays in Positive Economics, University of Chicago Press,Chicago.
Geels, F., 2002. Technological transitions as evolutionaryreconfiguration processes: a multi-level perspective and acase-study. Res. Policy 31, 1257–1274.
Geels, F., 2005. Technological Transitions and SystemInnovations: A Co-evolutionary and Socio-technicalAnalysis. Edward Elgar, Cheltenham.
Grubler, A., 1998. Technology and Global Change. CambridgeUniversity Press.
Grubler, A., Nakicenovic, N., Nordhaus, W.D. (Eds.), 2002.Technological Change and the Environment, RFF Press,Resources for the Future, Washington D.C. andInternational Institute for Applied Systems Analysis (IIASA),Laxenburg, Vienna
Hammond, D., 2003. The Science of Synthesis. University Pressof Colorado, Boulder, CO.
Hodgson, G., 1999. Evolution and Institutions. Edward Elgar,Cheltenham.
International Energy Agency (IEA), 2000. Experience Curves forEnergy Technology Policy. OECD, Paris.
Katz, M., Shapiro, C., 1985. Network externalities, competitionand compatibility. Am. Econ. Rev. 75 (3), 424–440.
Kemp, R., 1994. Technology and the transition to environmentalsustainability: the problem of technological regime shifts.Futures 26, 1023–1046.
Kemp, R., Schot, J.W., Hoogma, R., 1998. Regime shifts tosustainability through processes of niche formation: theapproach of strategic niche management. Technol. Anal.Strategic Manage. 10, 175–196.
Kemp, R., Rotmans, J., 2005. The management of the co-evolution of technical, environmental and social systems.In: Weber, M., Hemmelskamp, J. (Eds.), TowardsEnvironmental Innovation Systems. Springer, Berlin.
Lundvall, B.-A. (Ed.), 1992. National Systems of Innovation:Towards a Theory of Innovation and Interactive Learning.Pinter Publishers, London.
MacKenzie, D., 1992. Economic and sociological explanations oftechnological change. In: Coombs, R., Saviotti, P., Walsh, V.(Eds.), Technological Change and Company Strategies:Economic and Sociological Perspectives. Academic Press,(re-printed in MacKenzie, D., 1996. Knowing Machines:Essays on Technical Change. MIT Press, Cambridge, MA),pp. 25–48.
Martinez, N., 2006. Network evolution: exploring the change andadaptation of complex ecological systems over deep time.In: Pascual, M., Dunne, J.A. (Eds.), Ecological Networks:Linking Structure to Dynamics in Food Webs. Santa FeInstitute Studies on the Sciences of Complexity, OxfordUniversity Press, pp. 287–302.
Ministry of Economic Affairs (The Netherlands), 2004.Innovation in energy policy—energy transition: state ofaffairs and way ahead, available atwww.energietransitie.nl.
Nelson, R., 1993. National Innovation Systems: A ComparativeAnalysis. Oxford University Press, New York.
Nelson, R., 2002. Technology, institutions and innovationsystems. Res. Policy 31, 265–272.
Nelson, R., 2005. Technology, Institutions and EconomicGrowth. Harvard University Press.
Nelson, R., Sampat, B., 2001. Making sense of institutions as afactor shaping economic performance. J. Econ. Behav.Organ. 44, 31–54.
Nelson, R., Winter, S., 1982. An Evolutionary Theory of EconomicChange. Harvard University Press, Cambridge, MA.
Norgaard, R., 1994. Development Betrayed: The End of Progressand a Coevolutionary Revisioning of the Future. Routledge,London.
North, D.C., 1990. Institutions, Institutional Change andEconomic Performance. Cambridge University Press.
Norton, B., Costanza, R., Bishop, R., 1998. The evolution ofpreferences: why ‘sovereign’ preferences may not lead tosustainable policies and what to do about it. Ecol. Econ. 24,193–211.
e c o l o g i c a l c o m p l e x i t y 3 ( 2 0 0 6 ) 3 6 1 – 3 6 8368
Pierson, P., 2000. Increasing returns, path dependence, and thestudy of politics. Am. Polit. Sci. Rev. 94 (2), 251–267.
Rip, A., Kemp, R., 1998. Technological change. In: Rayner, S.,Malone, E.L. (Eds.), Human Choices and Climate Change,vol. 2. Battelle Press, Columbus, OH.
Simon, H.A., 1955. A behavioral model of rational choice. Quart.J. Econ. 69, 99–118.
Simon, H.A., 1959. Theories of decision making in economicsand behavioral science. Am. Econ. Rev. 49 (3), 253–283.
Simon, H.A., 1962. The architecture of complexity: hierarchicsystems. Proc. Am. Philos. Soc. 106, 467–482 (revised versionre-printed in Simon, H.A., 1996. The Sciences of theArtificial, 3rd ed. MIT Press, Cambridge, MA).
Simon, H.A., 1978a. Rationality as process and as product ofthought. Am. Econ. Rev. 68 (2), 1–16.
Simon, H.A., 1978b. Rational decision-making in businessorganisations, Nobel Memorial Lecture, December 8, 1978.
Simon, H.A., 1986. Rationality in psychology and economics. J.Business 59 (4(2)), S209–S224.
Tversky, A., Kahneman, D., 1986. Rational choice and theframing of decisions. J. Business 59 (4(2)), S251–S278.
Unruh, G.C., 2000. Understanding carbon lock in. Energy Policy28, 817–830.
Unruh, G.C., 2002. Escaping carbon lock in. Energy Policy 30,317–325.
van den Bergh, J., Ferrer-i-Carbonell, A., Munda, G., 2000.Alternative models of individual behaviour andimplications for environmental policy. Ecol. Econ. 32 (1), 43–61.
van den Bergh, C., Stagl, S., 2003. Coevolution of economicbehaviour and institutions: towards a theory of institutionalchange. J. Evol. Econ. 13, 289–317.
van der Brugge, R., Loorbach, D., 2005. A heuristic framework fortransition dynamics. In: Paper Ecological EconomicsSession, Complexity, Science and Society Conference,University of Liverpool, September 11–14.
von Bertalamffy, L., 1968. General System Theory: Foundations,Development, Applications. Georgr Braziller, New York.
Williamson, O., 1975. Markets and Hierarchies: Analysis andAntitrust Implications. Free Press, New York.
Williamson, O., 1985. The Economic Institutions of Capitalism.Free Press, New York.
Wilson, E.O., 1998. Consilience: The Unity of Knowledge. AlfredA Knopf, New York.
Winter, S., 1964. Economic natural selection and the theory ofthe firm. Yale Econ. Essays 4, 225–272.
