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Formalizing commonsense knowledge for reasoning about time has long been a central issue in AI. It has been recognized that the existing formalisms do not provide satisfactory solutions to some fundamental problems, viz. the frame problem. Moreover, it has turned out that the inferences drawn do not always coincide with those one had intended when one wrote the axioms. These issues call for a well-defined formalism and useful computational utilities for reasoning about time and change. Yoav Shoham of Stanford University introduced in his 1986 Yale doctoral thesis an appealing temporal nonmonotonic logic and identified a class of theories, causal theories, which have computationally simple model-theoretic properties. This paper is a study towards building upon Shoham's work on causal theories. We concentrate on improving computational aspects of causal theories while preserving their model-theoretic properties.

This multiple case study illustrates how ten selected industrial companies have managed to accomplish rapid growth after a long period (3–5 years) of slow growth. A particular aim was to determine whether these companies grew by adapting to the situation and responding to the demands of the market with their resources (effectuation) or by following previously determined plans and proceeding towards set goals (causation). Effectuation was originally connected to the creation of new business activities and an operating model covering the early stages of an organisation’s growth. However, recent studies have considered effectuation in the context of an existing business. This paper adds business growth to the context of established companies and explores the roles of effectuation and causation in their growth processes. The findings indicate the usage of both logics, but in nine of the ten companies’ effectuation influences as the dominant approach. Only one of the ten studied companies can be stated to follow the operating principles of causation.

More and more, established companies try to cooperate with start-ups, build their own, or try to imitate their mindsets. But, do they make decisions like expert entrepreneurs? Effectuation theory describes entrepreneurial decision-making, it has been popular in entrepreneurship research for the last two decades, but still underexplored in contexts such as established company’s decision-making. Therefore, this study answers the question of which factors affect the use of effectuation in established companies. Furthermore, the current use of the start-up decision-making principle is investigated. The research results show a higher use of effectuation over causation (alternative mechanism) in established companies. However, decision-making principles like “Mean orientation” and “Contingency orientation” are still dominated by causation. Identified factors for implementing effectuation in established companies are divided into six categories. In particular, effectuation requires a high flexibility and willingness to change goals. Using the already available means and resources of a company to pursue new goals, is another argument for using effectuation. Additionally, a more open and transparent culture, encouraging the identification and admittance of mistakes, also supports the use of effectuation. Based on this research, established companies should be able to understand better on which factors the implementation of effectuation depends and where it makes sense to use it.

Discussion on correlational methodologies often has focused on the validity of inferring causation from correlation, with some arguing that it is never valid (e.g., Holland, 1986; Kempthorne, 1978) and others arguing that it is valid under some conditions (e.g., Mulaik, 1993; Pratt & Schlaifer, 1984). A limitation of this sort of argument is that it tends to dichotomize the correlation–causation issue: inferring causation from correlation is valid or invalid. In contrast, I use a Bayesian approach that asks, “What is the probability of causation given correlation?” This approach brings out the importance of the size of the correlation, which has an important influence on the inverse probabilities of obtaining the correlation given that there is causation or lack of causation. As the size of the correlation increases, the probabilities of obtaining it when there is causation or lack of causation become increasingly different. Based on the size of the correlation and a variety of ways of characterizing this difference, I introduce the notion of the “limit ratio” and argue that it, in combination with the prior probability of causation, importantly influences the probability of causation given that a correlation has been obtained and the amount of information that can be gained by obtaining the correlation.

What is a cause in the social world? The received view is that when one thing happens, then another, and the first thing made the second happen — that’s cause. The received view works well for explaining the physical world. For example, why one ball will hit into another and make it fall into a side pocket. However, the received view offers no explanation for why anyone would want to hit a cue ball in the first place. Most people read David Hume as saying “look at things” instead of “accept authority” because authority couldn’t have come up with how things work except by looking at them. Empiricism, meaning “looking at things,” then is blamed on Hume. However, Hume makes it clear that the sensory observations of the eye are influenced by the emotions brought to the observation, leading to a distinction between sensations and perceptions. In this way, Hume was really calling for social scientists to “perceive things” rather than simply looking at them — aware of the fact that all empirical causality in the social world isn’t true, so much as a marriage of taken-for-granted assumptions and sensations. At least that’s how I read it, this guy’s work is crazy hard to read, and even harder to rewrite in some comprehensible way. Here, I take direct quotes from Hume, rewrite it in simplified English, and then interpret it for myself. I conclude that causation in the social sciences is wildly misunderstood by most social scientists who believe they have found a cause that exists outside of how they feel.

Stable neuronal assemblies are generally regarded as neural correlates of mental representations. Their temporal sequence corresponds to the experience of a direction of time, sometimes called the psychological time arrow. We show that the stability of particular, biophysically motivated models of neuronal assemblies, called coupled map lattices, is supported by causal interactions among neurons and obstructed by non-causal or anti-causal interactions among neurons. This surprising relation between causality and stability suggests that those neuronal assemblies that are stable due to causal neuronal interactions, and thus correlated with mental representations, generate a psychological time arrow. Yet this impact of causal interactions among neurons on the directed sequence of mental representations does not rule out the possibility of mentally less efficacious non-causal or anti-causal interactions among neurons.

Production is an essential notion for theory of mechanism, and to clarify its meaning brings about lots of difficulties. This article argues for an event-property theory which characterizes production as a particular property of event. That theory may provide a prospective solution for those difficulties.