Göktuǧ Morçöl

Penn State Harrisburg

gxm27@psu.edu

Presentation at the 74th National Conference of the American Society for Public Administration, New Orleans, LA, March, 2013

• On July 4, 2012, the scientists at the European Organization for Nuclear Research (CERN) declared that they had “nearly discovered” the Higgs boson

• aka., the “God Particle”

• With this near discovery, physicists came closest to precisely and definitively discovering a universal truth.

• The (near) discovery Higgs boson: a metaphor for the aspirations of the Newtonian/positivist science to achieve a universal, deductive, and quantitative/precise science.

• The question: Is it possible to discover the God Particle of Public Administration—the fundamental explanation of what Public Administration is all about?

• I answer the question from the perspective of complexity theory.

• The “Standard Model” of particle physics:

• The Higgs boson is responsible for the existence of all mass in the universe.

• To gain mass, all particles (protons, neutrons and electrons, quarks, and leptons) must pass through the “Higgs field.”

• The Higgs field needs a carrier particle to affect other particles: the Higgs boson.

• • CERN physicists “nearly discovered” this elusive particle.

• Probability of the physicists being wrong is one in over three million.

• But its is not certain whether they discovered the Higgs boson, or a “reasonable facsimile.”

• • This discovery process epitomizes the aspirations of the

Newtonian/positivist science.

• Universal (context-free) generalizations

• Deductive inference

• Quantitative/precise measurements

• The aspirations of the Newtonian/positivist science have been adopted by some public administration, policy studies, and political science scholars (Raadschelders: PAR article, 2011).

• The Waldo–Simon debate in 1952 is an early example over the discussions over these aspirations.

• Many examples show that Simon prevailed in the following decades: • See my paper

• Also Raadschelders and Lee article in PAR (2011)

• In my paper, I discuss the three aspirations of the Newtonian/positivists science:

Universal (and context-free) generalizations

Deductive inference

Quantitative/precise measurements

• In the context of two significant debates in recent decades:

o The debate on King, Keohane and Verba’s book Designing Social Inquiry: Scientific Inference in Qualitative Research (1994). The special issue of the American Political Science Review (1995)

o The debate on Flyvbjerg’s book Making Social Science Matter: Why Social Inquiry Fails and How It Can Succeed Again (2001). Schram and Caterino’s (Eds.) Making Political Science Matter: Debating Knowledge,

Research, and Method (2006)

• Two dimensions: • Unified science (unified logic and methodologies)

• Universal generalizations

• King, Keohane and Verba argue for a unified logic of inference both quantitative and qualitative research.

• The 19th century hermeneutic/phenomenological philosophers objected to this.

• Flyvbjerg objects to that also.

• Riccucci (Public administration: Traditions of inquiry and philosophies of knowledge, 2010) raises questions about a unified logic for public administration.

• The Newtonian/Positivist science favors universal (context-free) theories.

• King, Keohane, and Verba favor universal theories, but not strongly.

• Flyvbjerg argues that context-free theory is not possible in the social sciences.

• Flvbjerg cites the “Dreyfus paradox”:

• Why context makes it impossible for social scientists to formulate an “ideal theory.”

• An ideal theory has to exclude the context of everyday human activity (an ideal theory must be universal, abstract, and context-free) in order to make predictions,

• But by doing so it makes predictions impossible .

• In order to theorize in these ideal terms, context must be excluded, but any human action takes place in a context .

• Giddens’ double hermeneutic shows that context-free generalizations are not possible in the social sciences.

• There is a literature on the relations between the Newtonian/positivist science and quantitative methods.

• King, Keohane, and Verba frame extend the inferential logic of quantitative methods to qualitative methods.

• Flyvbjerg criticizes uses of emulating the methods of the natural sciences and favors qualitative methods, such as case studies.

• There are epistemological differences between quantitative and qualitative methodologies.

• Quantitative methods • Context can be removed

• Universal generalizations van be made

• More compatible with the Newtonian/positivist science

• Qualitative methods • Better for studying contexts

• Restrict generalizations

• More compatible with hermeneutic/phenomenological studies

The Problem of Complexity vs. Simplicity

• Can simplicity be achieved in theoretical physics? • The Occam’s razor

• Einstein’s formula of E = mc2

• Can simplicity be achieved in the social sciences? • King, Keohane, & Verba and others acknowledge complexities, but

argue that they can be simplified.

• CT: Reality is inherently and irreducibly complex. • Not only for social realities, but also for natural realities

• Two issues:

• Context of the observed

• Context of the observer

• CT: Knowledge of physical realities is contextual because observers are situated in the complex realities they observe

• Then could scientific laws be universal?

• Complexity theorists are not unified in their answers to this question:

• Kauffman: The “laws of complexity” can be discovered.

• Prigogine and Stengers: The knowledge of each system must include its unique history and behavior and the context surround them.

• The inherently complex and dynamic view of reality complexity theory presupposes does not leave much room for universal generalizations.

• Epstein and Axtell (Growing artificial societies, 1996):

• A science based on agent-based simulations should be “generative,” not deductive.

• The question is not to “explain” social phenomena with nomothetic generalizations, but how to “grow” them and understand generative processes within micro–macro processes using simulations.

• Axelrod (The complexity of cooperation, 1997): • Agent-based simulation provide a “third way of doing science”:

Like deduction, it starts with a set of explicit assumptions. But unlike deduction, it does not prove theorems. Instead, a simulation generates data that can be analyzed inductively. Unlike typical induction, however, the simulated data comes from a rigorously specified set of rules rather than direct measurement of the real world. While induction can be used to find patterns in data, and deduction can be used to find consequences of assumptions, simulation modeling can be used as an aid intuition. (pp. 92–93)

• Some complexity researchers use quantification heavily.

• Others use qualitative methods.

• They all recognize that a “Higgs-like” precision is not possible in predictions, however. • This is because of the inherent complexity of phenomena.

• Agent-based simulation researchers use their quantitative models: • Not to make universal generalizations,

• But to generate dynamic simulations about the systems they want to learn about.

• The storyline of Higgs boson is one that is universalist and deductivist and tests hypotheses quantitatively and with precision.

• The storyline of the Higgs boson is not applicable to public policy and administration studies.

• Scientific inquiry could, and should, seek contextual information, not context-free, universal generalizations.

• One can use quantitative methods, as well as qualitative methods: • But not in deductive theory testing.

• Instead, to generate models to better understand complex systems.

• Governance network theorists point to the complex and self-organizational nature of governance/policy networks and highlight the uncertainties in their behaviors

• Social network analyses are being used at an increasing frequency in the studies of complex governance networks

• Complexity theory contributes to these studies mainly in two ways:

• Provides an ontological and epistemological framework for them

• Bring up to the attention of researchers an additional method of inquiry: agent-based simulations

• Agent-based simulation (ABS) models have advantages and disadvantages compared to social network analyses (SNA).

• ABS models are inherently dynamic, as opposed to the relatively

more static, structural, nature of SNA.

• ABS models are more artificial (they make assumptions and

conduct simulations based on them), as opposed to the real-life data SNA researchers use.

• There are efforts to synthesize the strengths of ABS and SNA The

“Dynamic meta-network assessment and analysis” software being developed by Kathlen Carley and her colleagues at Carnegie Mellon University: http://www.casos.cs.cmu.edu/).

• My position: Combine them with qualitative studies.

Thank you!

Questions, comments?