In economics and management theories, scholars have traditionally assumed the existence of artifacts such as firms/organizations and markets. I argue that an explanation for the creation of such artifacts requires the notion of effectuation. Causation rests on a logic of prediction, effectuation on the logic of control. I illustrate effectuation through business examples and realistic thought experiments, examine its connections with existing theories and empirical evidence, and offer a list of testable propositions for future empirical work.

Causation and Effectuation: Toward a Theoretical Shift from Economic Inevitability to Entrepreneurial Contingency

Selection of relevant features and examples in machine learning

Scientific discovery learning is a highly self-directed and constructivistic form of learning. A computer simulation is a type of computer-based environment that is well suited for discovery learning, the main task of the learner being to infer, through experimentation, characteristics of the model underlying the simulation. In this article we give a review of the observed effectiveness and efficiency of discovev learning in simulation environments together with problems that learners may encounter in discovery learning, and we discuss how simulations may be combined with instructional support in order to overcome these problems.

/pdf/scientific-discovery-learning-with-computer-simulations-of-4lhduu757v.pdf

Scientific Discovery Learning with Computer Simulations of Conceptual Domains

http://cs.northwestern.edu/~paritosh/papers/sketch-to-models/klahr-dunbar-dual-space-search-cogsci-1988.pdf

Dual space search during scientific reasoning

A review on the application of deep learning in system health management

KEKADA, a system that is capable of carrying out a complex series of experiments on problems from the history of science, is described. The system incorporates a set of experimentation strategies that were extracted from the traces of the scientists' behavior. It focuses on surprises to constrain its search, and uses its strategies to generate hypotheses and to carry out experiments. Some strategies are domain independent, whereas others incorporate knowledge of a specific domain. The domain independent strategies include magnification, determining scope, divide and conquer, factor analysis, and relating different anomalous phenomena. KEKADA represents an experiment as a set of independent and dependent entities, with apparatus variables and a goal. It represents a theory either as a sequence of processes or as abstract hypotheses. KEKADA's response is described to a particular problem in biochemistry. On this and other problems, the system is capable of carrying out a complex series of experiments to refine domain theories. Analysis of the system and its behavior on a number of different problems has established its generality, but it has also revealed the reasons why the system would not be a good experimental scientist.

/pdf/experimentation-in-machine-discovery-1ie12z5ohl.pdf

Experimentation in machine discovery

Scheduled arriving aircraft demand may exceed airport arrival capacity when there is abnormal weather at an airport. In such situations, Federal Aviation Administration (FAA) institutes ground-delay programs (GDP) to delay flights before they depart from their originating airports. Efficient GDP planning depends on the accuracy of prediction of airport capacity and demand in the presence of uncertainties in weather forecast. This paper presents a study of the impact of dynamic airport surface weather on GDPs. Using the National Traffic Management Log, effect of weather conditions on the characteristics of GDP events at selected busy airports is investigated. Two machine learning methods are used to generate models that map the airport operational conditions and weather information to issued GDP parameters and results of validation tests are described.

https://ntrs.nasa.gov/api/citations/20110018181/downloads/20110018181.pdf

Modeling Weather Impact on Ground Delay Programs

Difficulty of deciding on control action depends on the weather and traffic conditions. Weather signature on different days can categorize days into days with little decision difficulty, days with moderate decision difficulty and days with high decision difficulty. This paper examines performance of different data mining methods in the three regions of decision difficulty. Not surprisingly, data mining methods have the best performance in the region of little decision difficulty and have the poorest performance in the region of most decision difficulty. In applications where data mining methods have differing performance in differing regions, it would be more useful to characterize the region specific performance instead of characterizing performance by a single parameter.

/pdf/data-mining-for-understanding-and-improving-decision-making-2j14wp3mfm.pdf

Data mining for understanding and improving decision-making affecting ground delay programs

Air traffic service providers have to make decisions regarding changes to air traffic flow in the event of major weather disturbances and traffic congestions to maintain safety of the system. The behavior of the air traffic management system will be more predictable if consistent decisions are made under similar traffic and weather conditions. Consistency of deciding on control action depends on the weather and traffic conditions as well as accuracy in predicting these conditions. Weather parameters (defined in terms of forecast and actual weather and traffic conditions) on different days can be used to categorize days into days with little decision consistency, days with moderate decision consistency and days with high decision consistency. Four years of traffic, weather and ground delay program decisions data at major airports in the United States are used in the analysis. This paper examines performance of different data mining methods in the three regions of decision consistency. Not surprisingly, data mining methods have the best performance in the region of most decision consistency and have the poorest performance in the region of little decision consistency. In applications where data mining methods have differing performance in differing regions, it would be more useful to characterize the region specific performance instead of characterizing performance by a single parameter. Finally, the results show no significant variation in the performance of different data mining methods for this particular problem. The fact that different mining methods show no significant variation also provides further confidence in the results of data mining methods. Work in this abstract discusses initial results. This paper describes the results in terms of both forecast and actual environmental conditions and discusses how prediction errors impact decision consistency.

/pdf/analysis-of-airport-ground-delay-program-decisions-using-2d02nvsoi5.pdf

Analysis of Airport Ground Delay Program Decisions Using Data Mining Techniques

The Processes of Scientific Research: The Strategy of Experimentation

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