"There is nothing like this book in the field today. Its remarkable contribution is to demonstrate that logical formalization can breathe new insights into a social science research program, even when it has attained a mature level of development. The book's process of logical reconstruction sheds light not only on the ecological paradigm, but also on other social science perspectivesboth within and outside organization studies."Martin Ruef, Princeton University
"It is vanishingly rare for organization theorists (social scientists more generally) to make such a big investment in regrounding theoryespecially when it is their own theory! This book really challenges the reader to think seriously about developing good theory, and about fixing the theory we have. I particularly appreciate the role given to the 'audience' in creating organizational forms, as well as the use of fuzzy sets to capture how categorization processes work. These new building blocks pay off in many fresh insights into longstanding issues. As such, the book is a huge service to the field."Ezra Zuckerman, Massachusetts Institute of Technology
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About the Author
Michael T. Hannan is the Stratacom Professor of Management in the Graduate School of Business and professor of sociology at Stanford University. László Pólos is professor of organization theory at the Durham Business School in the United Kingdom. Glenn R. Carroll is the Laurence W. Lane Professor of Organizations in the Graduate School of Business and (by courtesy) professor of sociology at Stanford.
Read an Excerpt
Logics of Organization Theory Audiences, Codes, and Ecologies
By Michael T. Hannan László Pólos Glenn R. Carroll Princeton University Press
Copyright © 2007 Princeton University Press
All right reserved.
Chapter One Language Matters
Sociological theorists rarely give explicit attention to the language used in formulating arguments and drawing conclusions. We argue that attention ought to be paid to the choice of a theoretical language. This chapter discusses such language matters; and it also argues that choice of a language matters for what a theory can express and whether (and how) it can unify fragmentary knowledge.
1.1 LANGUAGES FOR THEORY BUILDING
Sociological theories can be built and arguments can be unified without any special tools or methodologies. However, we believe that attempts in this direction face daunting challenges. The essential problem lies with language, with the natural language used in most sociological theory.
Natural languages such as English and Hungarian are less-than-perfect tools for eliminating ambiguity in the claims that one might wish to make. In fact, ambiguity normally comes in two varieties.
Lexical ambiguity. Although technical terms might be defined formally, the choice of words used in expressing their meanings brings about associations that soon generate deviations from the initial definitions. Such deviations lead, in turn, to ambiguities. We describe such ambiguity in detail in analyzing the concept of niche in Chapter 8. Structural ambiguity. Even when a language is freed from lexical ambiguities, and it "sounds" like the language of first-order logic, there is still room for structural ambiguity. For example, the sentence "There is a period of endowment for all organizations" can be interpreted in two ways. According to one reading, the endowment period is the same for all organizations; a second, more liberal, reading allows for different periods of endowments for the different organizations. In Chapter 7, we make clear that the choice between these readings matters for the explanatory power of the theory, so that it should be settled prior to implementing a formal approach.
As a result of such ambiguities, theoretical arguments in natural language admit multiple interpretations. Historians of classical sociological theory have written hundreds of papers on what Weber, Durkheim, or Marx really meant in certain passages. Such very close study of the passages does not appear to yield agreement on the interpretations of the sociological classics. The situation does not appear to differ for contemporary theories, which suggests that the natural-language statements are not getting any clearer.
Such unavoidable ambiguity makes it very hard to analyze complicated arguments in a natural language. Questions inevitably arise about whether some combination of premises does or does not warrant a given claim (or yield some inconsistency). It is notoriously hard to reason about the implications of complex structures stated in natural language. The chances of success in building coherent theories run higher if the arguments are stated in some kind of formal language. This is the strategy that we follow.
Success in efforts at formalization depends critically on issues of interpretation and translation. Translation from natural languages to constructed (artificial) languages is far from automatic. Introductory courses and texts in logic seek to train students in making consistent translations of indicative sentences, sentences that state facts (e.g., "Durham University is an organization"). Once students have learned the rules of this game, they are confronted with more complicated indicative sentences, e.g., "Every organization faces a competitor," whose translation requires learning the mysteries of quantification. But even the initiation into these mysteries leaves some problems unsolved. Translating indicative natural-language sentences to a logical language has to clear the hurdles posed by lexical and structural ambiguities. In other words, the sentences have to be interpreted. Formal languages are designed to rule out lexical and structural ambiguities; but the evolution of natural languages did not follow such guidelines.
Sometimes the logical structure of claims in a natural language appears on the surface; but often it is hidden. "Every person has a mother" indicates a quantificational structure on the surface. But "Men are mortal" does not. Both sentences have a conditional nature; but the conditionality is not always given on the linguistic surface by typical expressions such as "if ..., then ..." Although a course in (first order) logic might provide some guidance for identifying the logical structures of natural-language statements, one has to analyze the use of a sentence in scientific argumentation in order to excavate its logical structure. Finding the "intended" logical structure of a sentence requires interpretation. Sometimes it is sufficient to analyze the sentence to find such an interpretation. In other cases, attention to the theoretical context and examination of how a certain sentence is used in argumentation is needed to find the intended interpretation.
To this point, our examples share a tacit assumption of most introductory textbooks in logic, that the intended interpretation is available in logic (typically first-order logic). That is, the standard texts assume that the expressive power of the formal language is sufficient to distinguish among sentences with inferentially different behavior. But this need not be the case in real problems. For example, "Men are mortal" and "All men are mortal" might appear to be similar from one point of view. But in conjunction with "Socrates is a man," the second sentence leads to the conclusion that "Socrates is mortal," whereas the first sentence does not. "Men are mortal" might be sufficient to support the conclusion "Socrates is presumably mortal" in an appropriately chosen logic. Classical first-order logic does not have sufficient expressive power to account for this difference. Nonetheless there are logics with sufficiently fine-grained semantics to deal with this challenge.
Given an infinite variety of logics, no formalization can avoid the fundamental problem of choosing a formal language with adequate expressive power. Some typical problems in this respect can be analyzed with reference to the notion of extensionality.
Extensionality means that the replacement of names by other names with identical denotations (or predicates by other predicates with the same extension) yields logically indistinguishable expressions, that is logically equivalent formulas. Extensionality simplifies the logic, but it makes its notion of meaning rougher. In other words, the notion of logical consequence in first-order logic is less sensitive to nuance than are intensional logics (logics that relax the restriction to extensionality). Classical first-order logic, for example, cannot model the inferential impact of modalities such as perception or default, nor can it express the quantificational structures of rules with (unknown) exceptions, such as "Men are mortal."
Our study of relevant sociological theories suggests that considerations of genericity and modality arise regularly. Linguists refer to bare plural sentences such as "Organizations seek to seal off their technical cores from environmental uncertainty" as generic sentences, because they are general but not universal. These sentences typically express generality with (possible) exceptions. Modalities are expressed by sentential operators such as "possibly," "necessarily," and "allegedly." These modalities (or attitudes) do not fit into an extensional framework. Because genericity and modality appear to be ubiquitous in sociological theorizing, attempted translations of these theories into standard mathematics (whose underlying logic is the classical first-order logic) might not capture some key intuitions. Indeed, this problem might explain why formalizations in sociology so often seem to sterilize arguments, washing away the insights that made them appealing in the first place (Hannan 1997a).
A central challenge in formalizing and integrating sociological theories is to retain the core insights in making the translation from natural to formal languages. This task can be especially hard if the formal language is some kind of classical mathematics, such as algebra, calculus, or probability theory, as just noted. Fortunately, modern logic offers some appealing alternatives. The syntax and semantics can remain reasonably close to the natural-language argument; issues of genericity and modality can be handled systematically; and one can still "calculate," that is, derive implications, prove soundness, check for consistency, and so forth. For these reasons, we base our theory-building strategy on the use of modern logic(s).
In addition to choosing a language, a formalizer must adopt a strategy. A common misconception about strategy in formalizing theories comes from the mode of presenting formal work. The usual published report of a formal analysis begins by laying out definitions and assumptions and then uses them to derive lemmas and theorems. This mode of presentation suggests a mechanical process whereby deep insights follow from innocuous assumptions. Although this picture perhaps reflects a common belief about scientific activity, it seems hard to find cases of insightful theories that were actually generated in this way. In particular, insight seems to have no role in this view, and there is certainly no guarantee that anything derived in this manner will be insightful.
Because they more closely resemble scientific work as we know it, we prefer two alternative views of theory construction. We attribute both views to Imre Lakatos (1976, 1994). Both hold that insights occur first (in any imaginable manner) and then formalization is undertaken as a way to preserve, strengthen, and possibly amplify the impact of the insight.
Lakatos' first alternative sees the central insights of an argument as stemming from its conceptual definitions and the explanatory principles (causal stories) that link them. Here the formalization effort seeks to clarify how and when the explanatory principles operate by recasting them in a sharper language. As a result, the explanatory principles inherent in the insight might be discovered to be more or less consequential than originally thought; they also can be more readily compared to accounts based on other rival principles that attempt to explain the same phenomena. We illustrate this approach thoroughly in chapters on age dependence (Chapter 7) and the niche (Chapters 8 and 9).
The alternative view holds that the deep insights reside in the key theorems. Given a theorem containing such an insight, the formalization enterprise attempts to identify assumptions that might be used to derive it. In other words, the formalization effort works to create a formal argument that yields this "target" theorem as an implication. In the process, some assumptions might be found to be more plausible than others, just as some sets of assumptions might be more parsimonious than others. The chapters that analyze density dependence (Chapter 4), resource partitioning (Chapter 9), and structural inertia (Chapters 10 and 11) follow this strategy. Other chapters typically use a combination of the two approaches.
As these two views suggest, formalization does not automatically generate insight. Rather, it allows us to understand and sharpen insights already in mind by tracing ideas and concepts through chains of reasoning that might be hard (or even impossible) to follow precisely in natural language. For instance, one possible result of the target-theorem approach is that the formal analysis might show that a theorem does not follow from commonly accepted or empirically verified premises. On the other hand, if the target theorem can be shown to be implied by sensible premises, then the process of building the formal model might lead to unexpected insights as well. For example, fewer or different assumptions might be needed than was initially believed. Or the formalization might produce additional, previously unconsidered derivations that could be subjected to empirical scrutiny.
An important related point is that formal tools-including logics-can often be used to show inconsistencies or deficiencies in verbal theories that contain nontrivial chains of reasoning. This kind of demonstration makes little contribution, in our opinion. After all, a main reason for using logic and mathematics in science is to avoid the ambiguity of natural language. So no one should be surprised to learn that a "rational reconstruction" and analysis using mathematics and logic can uncover the deficiencies of natural language as an analytical tool. The greater challenge in using logic and other formal languages-the one that top modelers take on-is to deploy the formal tools to deepen the insights contained in a verbal theory, even if this takes considerable reconstruction. As the theory building in this book illustrates, reconstructing a verbal theory in a formal language is rarely a trivial, or even an easy, task.
1.2 USING DYNAMIC LOGIC
In the sociological context, an unusual feature of our work is its explicit use of modern logics. We now explain why we make this strategic choice.
The main role of logic in scientific applications is to clarify the notion of "logical inference" or that of a "sound argument." Many logics have been proposed over the last 2500 years. Most of them share a core idea: an argument is sound if and only if it is impossible that all of the premises are true and the conclusion is false. In other words, an argument is sound if the truth of the premises guarantees the truth of the conclusion. Given that various well-studied logics share this view, how can they differ? These differences arise from many sources. For instance, some logics construct and use different formal languages, others use somewhat different conceptions of truth, or different interpretation of what "impossible" means.
The first (more-or-less) formal system of logic, Aristotle's syllogistic logic, focused on reasoning about premises that express some kind of quantification. That is, it analyzed logical implication for sets of sentences that contain expressions such as "all," "some," and "none." (Recall the paradigmatic syllogism: "All men are mortal"; "Socrates is a man"; therefore, "Socrates is mortal.") Another Greek tradition of logic, that of the Stoics, dealt with sentential connectives such as "not," "and," and "if ... then."
Today logics similar to the Aristotelian and Stoic logics are called propositional logics, because they do not deal with the internal structure of the propositions. Modern logics, beginning with predicate logic (also called first-order logic) deal with quantification and sentential connectives and also analyze the internal structure of propositions. In a sense, predicate logic incorporates (modern) propositional logic as well the (modern) logic of quantification. Modern first-order logic, developed by the German philosopher Gottlob Frege, assigns interpretation to some components of the language somewhat differently than does the Aristotelian tradition.
Frege's first-order (predicate) logic provides a fairly general account of correct argumentation in mathematical reasoning. Because the reasoning in mathematics is possibly more accurate and better scrutinized than any other kind of reasoning, its formal model-first-order logic-became the logic for most scientists who pay attention to the formal details of argumentation. Because mathematics works with sharp definitions and its predicates are perfectly well characterized by their extensions, Frege constructed his logic (first-order logic) to be strictly extensional, even though it was Frege (1892) himself who noted the limitations of extensionality. In modern science and mathematics, first-order logic is the default; and it usually sits in the background. In other words, unless an analyst announces reliance on some other logic, we can safely assume that the logic to be used in analyzing the arguments is first-order logic. Moreover, our experience in the discipline leads us to think that first-order logic is the default in sociology as much as in any other science.
Excerpted from Logics of Organization Theory by Michael T. Hannan László Pólos Glenn R. Carroll
Copyright © 2007 by Princeton University Press. Excerpted by permission.
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Table of Contents
Language Matters 1
Languages for Theory Building 1
Using Dynamic Logic 5
Partial Memberships: Fuzziness 12
Organizational Ecology 18
Unification Projects 21
Audiences, Producers, and Codes 27
Clusters and Labels 29
Seeds for Categories and Forms 32
Similarity Clusters 41
Extensional Consensus 52
Complex Labels 56
Types and Categories 59
Intensional Semantic Consensus 67
Intrinsic Appeal and Category Valence 71
Forms and Populations 78
Test Codes and Defaults 79
Legitimation and Forms 84
Density Dependence Revisited 89
Identity and Audience 100
Identity As Default 101
MultipleCategory Memberships 107
Code Clash 109
Identities and Populations 110
Structure of the Audience 111
Nonmonotonic Reasoning: Age Dependence 121
A Nonmonotonic Logic 123
Beyond First-Order Logic 124
Nonmonotonic Reasoning 130
A Precis of the Formal Approach 133
Chaining Probabilistic Arguments 142
Closest-Possible-Worlds Construction 143
Integrating Theories of Age Dependence 150
Capability and Endowment 152
First Unification Attempt 157
Second Unification Attempt 163
Ecological Niches 169
Niches and Audiences 171
Tastes, Positions, and Offerings 174
Category Niche 177
Organizational Niche 178
Fundamental Niche 183
Implications of Category Membership 186
Metric Audience Space 187
Niches and Competitors 191
Realized Niche 193
Niche Overlap 194
Niche Width Revisited 198
Convexity of the Niche 203
Environmental Change 206
Resource Partitioning 209
Scale Advantage 210
Market Center 214
Market Segments and Crowding 215
Dynamics of Partitioning 220
Implications of Category Membership 226
Organizational Change 229
Cascading Change 231
Identity and Inertia 232
Organizational Architecture 235
Architecture and Cascades 239
Intricacy and Viscosity 246
Missed Opportunities 248
Change and Mortality 253
Opacity and Asperity 256
Limited Foresight: Opacity 256
Cultural Opposition: Asperity 261
Opacity, Asperity, and Reorganization 265
Change and Mortality 268
Niche Expansion 271
Expanded Engagement 271
Architectural and Cultural Context 276
Age and Asperity 278
Distant Expansion 279
Expansion and Convexity 281
Theoretical Unification 287
Common Conceptual Core 289
Inconsistencies Resolved 291
Theoretical Progress 293
Empirical Implications 298
Glossary of Theoretical Terms 305
Glossary of Symbols 313
Some Elementary First-Order Logic 321
Notation for Monotonic Functions 331
The Modal Language of Codes 334
What People are Saying About This
It is vanishingly rare for organization theorists (social scientists more generally) to make such a big investment in regrounding theoryespecially when it is their own theory! This book really challenges the reader to think seriously about developing good theory, and about fixing the theory we have. I particularly appreciate the role given to the 'audience' in creating organizational forms, as well as the use of fuzzy sets to capture how categorization processes work. These new building blocks pay off in many fresh insights into longstanding issues. As such, the book is a huge service to the field.
Ezra Zuckerman, Massachusetts Institute of Technology
There is nothing like this book in the field today. Its remarkable contribution is to demonstrate that logical formalization can breathe new insights into a social science research program, even when it has attained a mature level of development. The book's process of logical reconstruction sheds light not only on the ecological paradigm, but also on other social science perspectivesboth within and outside organization studies.
Martin Ruef, Princeton University