BUSI 2503 Section B Basic Finance for Non-Business Majors WINTER, 2015 Instructor: MICHAEL REYNOLDS Phone Number: (613) 851-1163 Email: [email protected] ...
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ECONOMIC OPPORTUNITY AND EVOLUTION BEYOND,LANDSCAPES AND BOUNDED RATIONALITY. The nature of economic opportunity has recently received significant attention in. entrepreneurship and strategy The notion of search on an NK opportunity. landscape has been particularly relevant to these conversations and debates We. argue that existing notions of landscapes are overly focused on bounded. rationality and search often instantiated as the problem of NP completeness. rather than focusing on how to account for the readily manifest emergent. novelty we see in the economic sphere the frame problem We discuss the. entrepreneurial and economic implications of these arguments by building on. unique insights from biology the natural and computational sciences. Key words entrepreneurship economic opportunity novelty strategy. 1 Introduction, The origins of novelty and nature of economic opportunity have recently received significant. attention in entrepreneurship strategy and organization science The notion of boundedly rational. search Simon 1955 1956 on a strategy landscape or phase space represents a particularly. powerful and influential metaphor and tool for thinking about the nature of economic activity 2 For. example NK modeling has been used to study how firms search locally or globally for peaks or. opportunities within landscapes Levinthal 1997 Winter et al 2007 Some have argued that. behavior and rationality on this landscape is a process Levinthal 2011 also see March 1994. Simon 1978 thus emphasizing mechanisms such as experiential learning and environmental. feedback while yet others have recently focused on how novelty and opportunity might emerge via. distant cognitive leaps on a landscape e g Gavetti 2011 cf Holyoak and Thagard 1996 The. discussion has centered on how economic actors navigate and map these opportunity landscapes. given uncertainty and such factors as the resources of the economic actors the cognition or biases of. the decision makers the dynamism of the environment or competition and past experience It is. important to note given the arguments in this paper that the origins of the landscape metaphor. and associated tools such as NK modeling can be traced back to computational and evolutionary. biology see Kauffman and Levin 1987 Kauffman and Weinberger 1989 3 Furthermore it is also. quite significant that Herbert Simon s 1955 1956 path breaking arguments about bounded. rationality were explicitly tied to biological intuition and mechanisms about organisms searching and. optimizing behavior in environments 4, The metaphor and very nature of an opportunity landscape have recently been challenged and. As we will later discuss phase spaces and various combinatorial landscapes have been central in a number of. disciplines including physics biology and chemistry see Reidys and Stadler 2002 To learn of the history and. basic mathematics behind phase spaces see Nolte 2010. Links between biology and economics of course are deep going back to Darwin and Malthus Mayr 1977 For a. history of the extensive links between biology and economics see Hodgson 2005. Importantly these arguments also provide the foundations for the field of artificial intelligence Newell Shaw and. Simon 1958 Newell and Simon 1959 for an overview see Russell and Norvig 2009. debated particularly in the context of explaining novelty in economic settings For example Winter. has raised questions about the notion of an opportunity landscape specifically vis vis Gavetti s. 2011 arguments and provocatively asks why we should even theorize opportunity 2012. 291 Winter raises concerns about such issues as the stationary and objective nature of opportunity. landscapes and extant mischaracterizations of rationality We share some of these concerns Scholars. have also asked questions about how we specify the phase space of strategies and novel activities that. are not actualized but nonetheless possible e g Bryce and Winter 2009 Winter 2011 further. argues that serendipity and contextual factors play an important role in the emergence of novelty. and in the discovery of profitable opportunities cf Denrell et al 2007 also see Winter 2012. Importantly scholars in entrepreneurship have also raised concerns that relate to the landscape. metaphor specifically in recent debates about the subjective versus objective nature of economic. opportunities whether opportunities are created and enacted versus discovered e g Alvarez. and Barney 2007 Alvarez Barney and Anderson 2012 Eckhardt and Shane 2012. The above debates raise important questions about the origins of novelty and nature of. economic opportunity Given that scholars have extensively used phase spaces or landscapes both as. a metaphor and tool such as NK modeling we explicitly revisit the underlying assumptions. embedded in these approaches and more generally revisit the idea of bounded rationality and. organism environment relations particularly as these apply to entrepreneurship and novel economic. activity We first discuss Herbert Simon s foundational notion of bounded rationality which partly. has led the field astray and his ideas about search and computational complexity in uncertain. environments We focus on the rationality related and biological and computational assumptions. made by extant theories that focus on search novelty and economic activity In short much of the. strategy and organizational literature is built on computation and algorithm oriented conceptions of. activity and behavior and we argue that these approaches suffer from some critical deficiencies We. address the weaknesses of these search and landscape focused views particularly vis vis. explaining novelty by highlighting arguments from the disciplines from which these approaches. stem biology physics and computer science While scholars have defined economic activity by. computational limitation and complexity for example focusing on NP completeness e g. Levinthal 2011 Rivkin 2000 cf Weinberger 1996 we argue that the real problem in explaining. novelty instead is the frame problem Along with highlighting concerns about the extant use of the. notion of search on a landscape or phase space we also draw some links between the approach. suggested herein and the aforementioned ongoing debates about the origins and nature of economic. opportunities e g Alvarez Barney Anderson 2011 Eckhardt and Shane 2012 Gavetti 2011. Gavetti et al 2012 Levinthal 2011 Winter 2011, To foreshadow our conclusion our focus is on the unprestatable but nonetheless scientifically. explicable nature of the phase or strategy space within which novel economic activity takes place. We highlight parallels between evolution in the biological and economic spheres respectively cf. Kauffman 1993 We argue that economic actions including behaviors products and capabilities. yield constant flows of emergent possibilities that cannot be meaningfully listed let alone. rationally considered searched and compared Both in economics particularly entrepreneurial. settings and in nature there is no effective procedure or algorithm that can list the opportunities. available for organisms and this non algorithmicity means that the emergent possibilities cannot be. prestated Thus the idea of search on a landscape or phase space and the very notion of bounded. rationality is highly problematic for explaining novelty However this does not leave us outside the. bounds of science As we will discuss explaining the origins of economic opportunities nonetheless. is possible We also seek to link these arguments with current debates about the nature of economic. evolution and opportunity, 2 Economic Activity Bounded Rationality and Computation in Uncertain Environments. The notion of bounded rationality has been central in advancing our understanding of. economic activity Herbert Simon s goal in introducing bounded rationality was to replace the. global rationality of economic man with a kind of rational behavior that is compatible with the access. to information and the computational capacities that are actually possessed by organisms including. man in the kinds of environments in which such organisms exist 1959 99 Rather than assuming. that organisms such economic actors are perfectly rational that is globally aware of all the. possibilities and able to comparatively compute them and decide optimally Simon emphasized the. search for possibilities and the localness and limits of rationality Bounded rationality of course has. subsequently become a central assumption of many economic and organizational theories including. transaction cost economics Williamson 1991 the behavioral theory of the firm March and Simon. 1952 and evolutionary economics Nelson and Winter 1982 The notion of bounded rationality. indeed provides a much needed contrast with and advance over models that assume perfect. rationality and explicitly focus on the efficiency of markets Arrow and Debreu 1954 The. assumption behind efficient markets particularly in its extreme form is that all possible goods and. services in effect all possible futures can be prestated and listed and that all of this can be. comparatively calculated and traded by economic actors 5. Simon s notion of bounded rationality is anchored on biological and computational language. mechanisms and metaphors For example Simon s original articles focus on organisms including. man in the kinds of environments in which such organisms exist 1955 99 Thus the argument is. meant to be general to include man and to highlight how organisms search and operate in. environments Not just the language is biological but so are the examples Simon s most extensive. Here we are highlighting a very specific extreme conception of markets We certainly recognize that alternative. conceptions exist including the behavioral ones we discuss For example the non equilibrium models of Hayek. 1945 provide one example, illustration of bounded rationality focuses on how an animal searches for randomly distributed food. in an environment or behavior space 1956 130 134 The basics of a behavioral model of. boundedly rational search were thus developed early on Note that this intuition is also quite closely. linked with mathematical models of animal foraging and optimization in patchy environments e g. Pyke Pulliam and Charnov 1977 6, The advantage of Simon s approach as a response to neoclassical rational choice models was. that it could be mathematized and formalized in powerful ways Organisms are seen as algorithmic. Turing machines that process information via programs within the bounds of their capability. Simon drew direct links between the way humans and computers solve problems which is readily. evident by the focus on concepts such as memory and storage capacity programs information. processing effectors and receptors see Newell Shaw and Simon 1958 cf Simon 1956. 1969 Simon provided a much needed alternative to perfectly rational conceptions of agents The. overly rational or even omniscient organism or economic actor was replaced by one who was. boundedly rational had computational limitations needed to search for solutions given limited. access to information about alternatives Note that these concepts of search and problem solving in. environments effectors and receptors learning also provide the very foundations of the field of. artificial intelligence see Russell and Norvig 2009 chapter 2. Our understanding of economic activity continues to be influenced by the notion of bounded. rationality and by direct analogies and tools from the biological and computational sciences As. discussed at the outset NK modeling was originally developed in evolutionary and computational. biology Kauffman and Levin 1987 Kauffman and Weinberger 1989 and this tool is now. frequently used in strategy and organization science e g Levinthal 1997 also see Gavetti et al. The organizational and economic sciences continue to make extensive use of biological tools concepts and. mechanisms For example beyond Simon s notion of bounded rationality fields such as organizational ecology. have borrowed and focused on concepts quite familiar to us from biology such as population level dynamics. resource partitioning niches carrying capacity and fitness and selection Carroll and Hannan 2000 for an. overview of these concepts see Carroll 1984 Singh and Lumsden 1990. 2005 Levinthal and Warglien 1999 Rivkin 2000 Rivkin and Siggelkow 2002 Siggelkow and. Rivkin 2005 Winter et al 2007 This work has indeed generated many important insights about. how firms and economic actors behave and search and find profitable opportunities in. landscapes The most basic mechanism for exploring the landscape has focused on experiential. learning where focal actors learn and adapt as they experience and sample the landscape itself and. receive behavioral feedback from the environment cf Levinthal 1997 This research has for. example focused on the problem of getting stuck on sub optimal local peaks The contrast between. local exploitation versus more global exploration on landscapes has also been a central metaphor for. understanding the tradeoffs that firms make e g Levinthal and March 1993 Rivkin and Siggelkow. 2007 These approaches can broadly be classified as part of evolutionary economics as well as a. more general behavioral program of research in organization science and strategic management for. an overview see Gavetti et al 2012, There are however also some important tensions within this program of research particularly. vis vis the respective emphasis that ought to be placed on the rationality of an organism itself. versus the environment Gavetti 2011 has recently argued that an emphasis should be placed on. cognitive leaps that economic actors can make on landscapes cf Holyoak and Thagard 1996 The. focus is on finding ways to capture the crude but forward looking representations that economic. actors have about operating on uncertain opportunity landscapes cf Gavetti and Levinthal 2001. This more organism centric approach strives to place some emphasis on agency and cognition and. more general search on a landscape in response to the seemingly more deterministic approaches. that characterize evolutionary economics Winter 2011 responds to Gavetti s general emphasis on. more rational cognitive search on landscapes and argues that serendipity and contextual factors play. a central role One of the central tensions in this discussion is how much rationality to afford. organisms and economic actors that is the role of organisms or actors versus randomness. serendipity and luck, It is worth making a specific note of the fact that much of the above discussion and large. swaths of evolutionary economics and organization science more generally is based on a one to. one importation of theories mechanisms and tools from the biological and computational sciences. The links between economics and biology have indeed been quite tight going back to Darwin and. Malthus see Mayr 1977 Evolutionary economics has indeed been an effort to generalize the basic. framework of Darwinism and the emphasis on environmental selection for a recent overview see. Hodgson and Knudsen 2011 Similarly the mechanisms and tools in artificial intelligence and. biology are also quite readily apparent in much organizational work a focus on the computational. and algorithmic aspects of behavior and decision making In all of the above the notion of a phase. space has been central a representation of all possible actions for organisms and their exploration of. these landscapes through search and various cognitive mechanisms. While the links between biology and economics have been fruitful we argue that the phase. space notion and associated tools utilizing various forms of search such as NK modeling are. problematic We build on arguments from the biological natural and computational sciences to. make our point, 3 1 What is the Nature of the Problem From NP to Frame. How specifically should the problem of explaining economic activity opportunities and. novelty in particular be conceptualized We argue that the extant focus on computation and. algorithmic search and behavior embedded in arguments about bounded rationality is. problematic While bounded rationality rightly amends models of global rationality by setting limits. to both what can be considered and the abilities of actors to process all the relevant information. nonetheless different foundations are needed Specifically we hope to amend the focus on bounded. rationality and computational complexity to also consider the generative and productive aspects. beyond search and calculation manifest in economic activity. In existing work economic actors and firms are treated as algorithmic information. processors Economic actors and firms are in effect seen as Turing machines To illustrate strategy. scholars and organization scientists have specifically focused on bounded rationality in the form of. the unfeasible computability of all choices and their interactions e g Levinthal 2012 Rivkin. 2000 known as the NP problem in computer science problems that may be computable but only. in too long exponential a time due to complexities 7 The setting of NK landscapes indeed is. optimal for studying the NP complete problem cf Weinberger 1996 But the very premise of NP. completeness is problematic in the context of economic activity just as it is in the context of. biological activity That is the NK approach presumes that solutions pre exist the landscape is. given and needs to be searched or calculated and that all options are somehow listable The. economic problem is framed as one where all solutions are listable searchable and comparable. though where the processing or comparison of all these solutions occurs in bounded fashion This. boundedness focuses on the limits of calculation and the impossibility of considering all possibilities. for example as illustrated by the combinatorial interactions of various decision elements proxied by. K in NK work NP completeness and the more general emphasis on search on a landscape thus. frames the central economic problem as one of information processing and cognitive limitation The. landscape itself is seen as given and the economic problem is computational and algorithmic A. satisficing and thus more limitedly rational solution can be calculated or learned over time. within the limitations of the computational power of the agent involved. Note that the exercise of computing solutions has some striking similarities with neoclassical. economics and rational choice approaches namely the emphasis on computation While. equilibrium oriented models focus on the simultaneous and instantaneous nature of this economic. calculation evolutionary and computational approaches in turn focus on the temporal cognitive and. For an additional example of this type of complexity in economic settings see Axtell 2005. search related aspects of the climb to an optimum We certainly find the latter conceptualization. more convincing But it also particularly vis vis explaining novelty in economic settings deserves. The notion of NP completeness mis specifies what the economic problem entails While. complexity is involved in economic decision making the problem is not one where all or even just. some solutions are listed listable and comparable but rather one of how we can account for the. emergence of these solutions in the first place The shift then if we seek to retain the landscape. metaphor is one of understanding how portions of the landscape hidden to our view emerge in. the first place, The central problem in question then is not NP complete Rather we should instead focus on. the frame problem originally introduced by McCarthy and Hayes 1969 for a broader sense of the. frame problem see Dennett 1984 8 Put simply the frame problem focuses us on the problematic. nature of explaining the full task set of activities and possible functionalities and uses for operating in. the world or some situation or environment whether real or artificial The problem is that there is. no full account or set of algorithms that can be given about all possible actions uses and functions. The shift here is also one of needing to move from an emphasis on the exogenous environment to. endogenous nature Felin 2012,We are not just talking about the narrow problem. To illustrate the incapacity to solve the frame problem algorithmically consider the familiar. screwdriver cf Longo Montevil and Kauffman 2012 Suppose we try to list all its uses alone or. with other objects or processes screw in a screw wedge open a door open a can of paint tie to a. stick as a fish spear rent to locals and take 5 of the catch kill an assailant and so forth As we will. For a broader conception of the frame problem see Dreyfus 2007 In short Dreyfus highlights how the frame. problem focuses on which facts are relevant in a given situation something that computers cannot meaningfully. bootstrap the problem of the situation specific nature of frames and the problem of how to account for operating in. a changing world This broader conception of the frame problem indeed is the one that we have in mind. argue below with reference to biology or to phenotypes as forms and functions of the living the. number of uses of a screwdriver as forms and functions of uses and activities are both indefinite. and unorderable No effective procedure or algorithm can list all the uses of a screwdriver This. means a fortiori that the frame problem is not solvable algorithmically However as we discuss. below evolution in nature solves the frame problem non algorithmically But importantly. because we cannot list all the uses of evolving cellular or molecular screwdrivers we cannot prestate. all the possibilities and thus do not and can not know the sample space of the process and therefore. can make no probability statements in any known way Not only do we not know what will happen. we typically do not know what can happen Yet we argue it is from the unprestatable uses of. screwdrivers in general that economic novelty emerges. In the economy the landscape metaphor and associated computational tools require every. observable in a given environment i e the possible space or landscape to somehow be listed and. classified and assigned its proper uses and functionalities To put this in more practical terms every. object is a has a needs a But this list of possible affordances is not fully prestatable for. operating in the world other than for extremely limited circumstances The problem is not only one. of comparison amongst the best uses and functions of objects and spaces but even the very. generation of the full list is not algorithmically feasible Or to put this differently as discussed by. Gibson to perceive an affordance is not to classify an object 1986 134 Thus the problem is not. one of informational complexity and computational limitation NP completeness though these of. course also play a role in certain types of behavior Rather the problem is that the landscape itself is. not listable or predefinable The problem then shifts to explaining the origins of uses and functions. particularly new ones An artificial agent of course might be given tools to generate hypotheses about. possible uses and functionalities via mechanisms such as trial and error or association But these are. scarcely sufficient for explaining economic or entrepreneurial novelty Felin and Zenger 2009. Outcomes are only as good as the intelligence of the interpreter This matters since presently the. mechanisms used in artificial intelligence are the same as those used in our study of human behavior. see Russell and Norvig 2009, Scholars have long been optimistic about the potential of artificial intelligence and computers. to surpass the ability of humans But beyond the frame problem it is hard to program and ascribe. any meaningful form of creativity or novelty to artificial agents Dreyfus 1992 The future. problems related to the generation of novelty were even anticipated by the very early pioneers of. artificial intelligence such as Ada Lovelace She argues that the Analytical Engine has no. pretensions whatever to originate any thing It can do whatever we know how to order it to perform. It can follow analysis but it has no power of anticipating any analytical relations or truths Its. province is to assist us in making available what we are already acquainted with 1848 722. The desire to capture biological and human activity including entrepreneurial and economic. such in computational or mechanistic form of course is tempting and seemingly scientific and. represents a more general ethos of trying to unify the sciences through computational. reduction Works such as Jacques Loeb s 1912 Mechanistic Conception of Life capture this. intuition a heroic attempt to build a general theory focused on environmental inputs stimulus. response relationships and selection These mechanistic conceptions and the focus on computational. observables also have links to prominent theories and approaches in psychology Skinner 1938 and. physics Mach 1897 More broadly the environment oriented conceptions of evolutionary. economics suffer from similar problems Felin and Foss 2012 While these approaches are. influential they are overly deterministic and unable to explain the emergence of variety. 3 2 The Origins of Variety and Novelty Insights from Biology. Where then does variety and novelty whether in the biological or economic sphere. originate from Perhaps the best place to start is with reference to extant biological arguments that. deal with similar questions about the origins of variety and novelty in nature Evolutionary models. that focus on selection require a counterpart to explain where the selection set comes from In other. words both in biology and economics we need to not just explain the survival of the fittest but also. the arrival of the fittest cf Fontana and Buss 1995 Radical and emergent heterogeneity in. nature is not explainable by appealing to the mechanism of selection alone cf Kauffman 1993 9. As areas such as evolutionary economics are attempting to build on a general theory of evolution. Winter 2011 it is important to highlight both sides of the argument arrival or development and. survival 10, Our argument building on extant biology is that there are selection independent mechanisms. that generate novelty in the biosphere These arguments do not require a mind or intention or. purposiveness Rather novelty is an emergent process observable in nature and readily extendable. into the domain of understanding entrepreneurial and economic activity. Recall first that Darwin s Origin of Species is founded on two principles descent with. modification and selection The first is as revolutionary as the second It stresses the idea that. Within the domain of biology and nature the arrival of variety has focused on a host of factors For example. scholars have highlighted such factors as niche construction Odling Smee et al 2003 speciation and punctuated. equilibria Eldredge and Gould 1977 exaptation Gould and Vrba 1982 epigenetics Waddington 1942. ontogeny Gould 1999 and morphogenesis or the growth of form Thompson 1917 Turing 1952 In section 4. we revisit extant work in economics that touches on these issues In all these approaches the role of randomness is. crucial in producing biological variability and diversity it goes from Turing stochastic fluctuations in. morphogenesis to Gould s broad notion of contingency. In terms of general Darwinism and the important of selection as a mechanism Winter has argued that. natural selection and evolution should not be viewed as concepts developed for the specific purposes of. biology and possibly appropriable for the specific purposes of economics but rather as elements of the. framework of a new conceptual structure that biology economics and other social sciences can comfortably. share 1987 617, We consider that Darwin s first principle descent with modification a key component of variability and diversity in. evolution should also be given a similar fundamental role. organisms beginning with unicellular ones proliferate with variation under all circumstances This. radically changes the previous evolutionist perspective by Buffon and Lamarck as variation was. supposed to be induced by the environment Then of course Darwinian selection as the exclusion. of the incompatible applies, To illustrate consider the emergence of the swim bladder Kauffman 2008 Longo Montevil. and Kauffman 2012 Swim bladders help fish maintain neutral buoyancy via the ratio of water to air. in the bladder cf Perry et al 2001 This functionality a Darwinian preadaptation emerged from. lungfish as some water seeped into lungs Sacs in the lungs were partially filled with air and with. water poised to evolve into swim bladders The possibility of developing this new and emergent. functionality existed a priori but was not a necessity life could continue without it for that particular. fish But the novel functionality was an adjacent possibility once lungfish existed but not before a. mutation or other forms of inheritance made that possibility actual as well as heritable In other. words the bladder represents a preadaptation that as an adjacent possible emerges without selection. acting to achieve the possibility It is a possibility enhanced by reproduction with heritable. variation So both new functionalities and niches may emerge possibly originating at molecular. level mutations These new observable phenotypes are thus totally unpredictable they may even. depend on a quantum event in a germinal cell Buiatti and Longo 2013 11. Organisms in nature constantly develop surprising functional capabilities and uses that are not. prestatable The swim bladder once it has evolved may constitute an adjacent possible empty niche. where for example bacterium may evolve to live Once evolved the swim bladder alters the. adjacent possible evolution of the biosphere But what is the role of natural selection here. Selection surely plays a role in the evolution of a population of lungfish to craft a working swim. Another famous example by Gould see Gould and Vrba 1982 is the formation of the vertebrate ears bones. They derive by exaptation ex post adaptation from the double jaw of some vertebrate some 200 millions years. ago A typical case of Gould s contingency there was no need for animals to have ears and the process required. cascades of random mutations and many other explorations possibly excluded by selection The animals. interactions and thus the ecosystem changed by this new phenotype Some today can sell Mozart s recorded piano.
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