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

Data Mining Practical Machine Learning Tools and Techniques

计量经济分析 = Econometric analysis

In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Theory in Practice: Increasing Professional Effectiveness

Strategic tools are indispensible for business and competitive analysis. Yet we know very little about managers' internal logic as they put these tools into practical use. We situate our study in a business school context using action learning prior to the manifestation of practice to complement our understanding of practice. Using Personal Construct Theory and Repertory Grids, our mid-range theorizing showed that, contrary to current thinking about strategic tools, managers think in dualities (often paradoxically) and have a preference for multiple-tools-in-use, tools that provide different perspectives, peripheral vision, connected thinking, simultaneously help differentiate and integrate complex issues, and guide the thinking process. These findings are important for designing better tools and the nurturing of critical managerial competencies needed for a complicated world. Our study's focus also has wider implications for scholars as we see our own material evaluated by those who will put these lessons into practice.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-6486.2012.01082.x

How useful are the strategic tools we teach in business schools

Drawing on the social capital literature, this study develops a new measure to assess the internal social capital using a sample of family firms and its effect on economic and noneconomic performan...

/pdf/a-measure-of-variations-in-internal-social-capital-among-o1yjkixt7h.pdf

A Measure of Variations in Internal Social Capital among Family Firms

Goals or aspirations and their relationships to risk taking and performance are important issues in both psychology and strategic management. The concept of adaptive aspirations, as discussed in Cyert and March's Behavioral Theory of the Firm, has long been a topic of interest in both fields. Moreover, many studies in strategy have focused on risk and/or extreme performance. In the current paper, we build on earlier models of adaptive aspirations. We introduce into the models a new risk preference function that incorporates changes in risk preference at extremes of performance. Based on empirical studies and the managerial literature, we also introduce alternative strategies for setting reference groups. Simulations of the resulting models suggest important differences in outcomes from earlier studies and this invites further empirical investigation. These simulations also have significant implications for managerial goal setting. Copyright © 2011 John Wiley & Sons, Ltd.

Adaptive aspirations: performance consequences of risk preferences at extremes and alternative reference groups

Organizations learn and adapt their aspiration levels based on reference points (prior aspiration, prior performance, and prior performance of reference groups). The relative attention that organizations allocate to these reference points impacts organizational search and strategic decisions. However, very little research has explored this. Therefore, we build a recursive feedback model of learning from organizational experience that explains heterogeneity of attention allocation to the reference points in adaptive aspirations. In a sample of the German magazine industry (1972–2010), we find when early in their life cycle and as they or their parent company age, organizations tend to focus more on their own aspirations; however, when at the verge of bankruptcy, they increase their attention to competitors' performance.

Adaptive aspirations and performance heterogeneity: Attention allocation among multiple reference points

(2003). Sticky Knowledge. Barriers to Knowing in the Firm. Innovation: Vol. 5, No. 2-3, pp. 287-288.

Daniela Blettner

Papers

How useful are the strategic tools we teach in business schools

A Measure of Variations in Internal Social Capital among Family Firms

Adaptive aspirations: performance consequences of risk preferences at extremes and alternative reference groups

Adaptive aspirations and performance heterogeneity: Attention allocation among multiple reference points

Sticky Knowledge. Barriers to Knowing in the Firm