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Microfoundations and Measurement for...

Manian, Venkatesan Govind.

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Microfoundations and Measurement for Ambiguity in Communication with Application to Social Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Microfoundations and Measurement for Ambiguity in Communication with Application to Social Networks./
作者:	Manian, Venkatesan Govind.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	100 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-01, Section: A.
Contained By:	Dissertations Abstracts International83-01A.
標題:	Language. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28550778
ISBN:	9798522940171

Microfoundations and Measurement for Ambiguity in Communication with Application to Social Networks.
Manian, Venkatesan Govind.

Microfoundations and Measurement for Ambiguity in Communication with Application to Social Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 100 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: A.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Extant research often models ambiguity - that one object can have multiple interpretations - as "noise" or "optional" (e.g. McMahan and Evans 2018, Reagans and Zuckerman 2008). This stance is in tension with evidence that ambiguity is common, existing to varying degrees, and consequential and therefore better thought of as "more or less" rather than "yes or no". Predictably, literatures that seek to bracket ambiguity find themselves without the analytical tools to specify core theoretical claims and devise measurement strategies. This dissertation works towards taking ambiguity seriously by introducing microfoundations for its analytical specification, a measurement strategy at the level of the word, and application of these microfoundations and measurement strategy in the "brokerage" literature (Burt 1982), which serves as a strategic theoretical site to demonstrate the benefits and broader ramifications of this orientation. More specifically, Chapter 2 begins by reviewing evidence for the claim that ambiguity is common and consequential. Evidence that ambiguity is common is strongest where actors seek to remove it: in organizations and in science. Ambiguity poses a major challenge for coordination across disciplinary boundaries or functional groups, in which the same physical, semantic, or representational object has different interpretations for different communities. Despite organizations' best efforts, the "knowledge transfer" (e.g. Carlile 2004) literature finds consistently that minimal differences in task, role, or training result in barriers that imperilcoordination. Similarly, evidence from scientific research indicates that ambiguity is not only common but generative for research across boundaries and necessarily produced as researchers import concepts from other disciplines or seek to synthesize fields. I further review evidence that ambiguity is consequential. Studies of how products and companies and other offerings fit within different categories show that the consequences of ambiguity vary from positive to negative but are rarely neutral. Similarly, research from social networks demonstrates that ambiguity is often cultivated, both for control -- as in the case of Cosimo de Medici (Padgett and Ansell 1993) -- and for coordination -- as in the case of music producers (Long Lingo and Mahoney 2010). This evidence motivates that ambiguity is best modeled as a first-order concern, rather than an optional curiosity. Not all cases of ambiguity are semantic in nature -- that is, where one word or phrase has multiple interpretations -- but for those that are, I introduce microfoundations from linguistics that analytically characterize ambiguity as a non-conventional signaling device with multiple conventional meanings. This specificity allows us to state desiderata for a measurement strategy: measuring differences in connotations between groups for common words, inferred from context.Chapter 3 details that "word embedding models" (or "embeddings") meet these desiderata. I provide a high level explanation of what embeddings are and why they work, intended for the interested lay reader with some familiarity with text analysis. I then provide evidence that embeddings measure ambiguity consistent with external benchmarks and between multiple groups. However, a common pitfall in working with embeddings is that they require enormous corpora. I describe how to navigate the tradeoffs inherent in working with embeddings for "smaller data", on the order faced in Chapter 4.

ISBN: 9798522940171Subjects--Topical Terms:

643551
Language.

Microfoundations and Measurement for Ambiguity in Communication with Application to Social Networks.
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