Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

The 2008 crash has left all the established economic doctrines - equilibrium models, real business cycles, disequilibria models - in disarray. Part of the problem is due to Smith’s "veil of ignorance": individuals unknowingly pursue society’s interest and, as a result, have no clue as to the macroeconomic effects of their actions: witness the Keynes and Leontief multipliers, the concept of value added, fiat money, Engel’s law and technical progress, to name but a few of the macrofoundations of microeconomics. A good viewpoint to take bearings anew lies in comparing the post-Great Depression institutions with those emerging from Thatcher and Reagan’s economic policies: deregulation, exogenous vs. endoge- nous money, shadow banking vs. Volcker’s Rule. Very simply, the banks, whose lending determined deposits after Roosevelt, and were a public service became private enterprises whose deposits determine lending. These underlay the great moderation preceding 2006, and the subsequent crash.

The Economic Institutions of Capitalism

What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

In this innovative approach to the practice of social science, Charles Ragin explores the use of fuzzy sets to bridge the divide between quantitative and qualitative methods. Paradoxically, the fuzzy set is a powerful tool because it replaces an unwieldy, "fuzzy" instrument—the variable, which establishes only the positions of cases relative to each other, with a precise one—degree of membership in a well-defined set. Ragin argues that fuzzy sets allow a far richer dialogue between ideas and evidence in social research than previously possible. They let quantitative researchers abandon "homogenizing assumptions" about cases and causes, they extend diversity-oriented research strategies, and they provide a powerful connection between theory and data analysis. Most important, fuzzy sets can be carefully tailored to fit evolving theoretical concepts, sharpening quantitative tools with in-depth knowledge gained through qualitative, case-oriented inquiry. This book will revolutionize research methods not only in sociology, political science, and anthropology but in any field of inquiry dealing with complex patterns of causation.

Fuzzy-Set Social Science

This study examines the effect of online reviews on new product sales for consumer electronics and video games. Analyses of panel data of 332 new products from Amazon.com over nine months reveal that the valence of reviews and the volume of page views have a stronger effect on search products, whereas the volume of reviews is more important for experience products. The results also show that the volume of reviews has a significant effect on new product sales in the early period and such effect decreases over time. Moreover, the percentage of negative reviews has a greater effect than that of positive reviews, confirming the negativity bias. Thus, marketers need to consider the distinctive influences of various aspects of online reviews when launching new products and devising e-marketing strategies.

The Effect of Online Consumer Reviews on New Product Sales

As one of the big emerging markets, China’s enormous population and rapid increase in consumer spending have attracted many multinational corporations (MNCs). Meanwhile, the misconception of China as a homogeneous market often leads to difficulties in assessing market demand and enacting effective strategies. Examines the diversity among Chinese consumers across seven regional markets. Data from a national survey suggest that consumers from various regions are significantly different from one another in terms of purchasing power, attitudes, lifestyles, media use, and consumption patterns. MNCs need to take a cautionary approach when expanding into the inland regions, and must adapt to the local market conditions and devise sustainable strategies.

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Regional market segments of China: opportunities and barriers in a big emerging market

In this study we examine the contingent relationship between R&D intensity and performance of international joint ventures (IJVs) in an emerging market context. Based on Teece's (1986) arguments regarding the appropriability of innovation, we identify two types of appropriability hazard related to IJVs’ R&D activities in this context: local-market-related and local-partner-related hazards. We argue that a positive relationship between R&D intensity and IJV performance is more likely to occur if these appropriability hazard can be mitigated. Results using a sample of manufacturing IJVs in China provide support for these arguments. We find that R&D intensity is positively related to performance in export market-focused IJVs but not in local market-focused IJVs. In addition, using a configuration approach, we find that R&D intensity is positively related to performance in IJVs that have an export market focus and in which the multinational companies (MNCs) have a majority ownership, but not in other market focus-ownership structure configurations. These findings contribute to our knowledge of R&D activities of MNCs’ overseas subsidiaries.

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R&D intensity and international joint venture performance in an emerging market: moderating effects of market focus and ownership structure

Measuring intercultural effectiveness: An integrative approach

How to integrate the emerging consumer segments in transitional economies into multinational corporations’ global marketing strategies presents a significant challenge. An analysis of China’s urban consumers based on a 1997 national survey reveals several market segments that are distinctive in their demographics, psychographics, lifestyles, media usage, and consumption patterns. The findings suggest that multinational corporations need to adapt to the local market conditions in China and other transitional economies.

Geng Cui

Papers

The Effect of Online Consumer Reviews on New Product Sales

Regional market segments of China: opportunities and barriers in a big emerging market

R&D intensity and international joint venture performance in an emerging market: moderating effects of market focus and ownership structure

Measuring intercultural effectiveness: An integrative approach

Emerging Market Segments in a Transitional Economy: A Study of Urban Consumers in China