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How do entrepreneurs use simple rules to build their business models? Based on an inductive study of three Chinese Internet and technology firms, the authors find that business models emerge from simple rules that entrepreneurs learn from their experience. Simple rules also guide entrepreneurs to actualize and exploit opportunities in the marketplace, and they can help business models evolve through market feedback, especially in internationalization. This paper aims to delve into the black box of entrepreneurial decision-making and offer a better depiction of the business model development process in uncertain and fast-changing environments and thus provide guidance for future entrepreneurs.

Following the case method (Eisenhardt, 1989; Yin, 2003), the authors first present a thick description of characteristics of three companies and the dynamics of their business models. They then code these descriptions into first-order measures. Finally, they aggregate these measures into abstract constructs. They constantly compare the theoretical constructs and the emerging theory with the existing literature on business models.

The authors generate three key insights from the findings: business models emerge from simple rules learned from entrepreneurs’ experience, simple rules help entrepreneurs exploit and actualize opportunities in the marketplace and simple rules help businesses expand and evolve business models through market feedback, especially in internationalization.

This paper falls into the intersection of strategy and entrepreneurship – an emerging new field of strategic entrepreneurship – and is concerned with how businesses create and sustain a competitive advantage while simultaneously identifying and exploiting new opportunities. The authors bring people – the individual decision-makers for businesses – back in strategy research and depict a more realistic picture of the behavior of successful entrepreneurs and their business model development process.

So far, the simplicity of heuristics has been mostly studied at the rule level. However, actors' bounded rationality implies that small bundles of rules drive behavior. This study thus conducts a conceptual elaboration around such bundling. This leads to reflections on the various processes of heuristic emergence and to qualifications of the respective characteristics of basic heuristic classes.

Determining which rules – out of many possible ones – to select in one's small bundle constitutes a difficult combinatorial problem. Fortunately, past research has demonstrated that solutions can be found in evolutionary mechanisms. Those converge toward bundles that are somewhat imperfect yet cannot be easily improved, a.k.a., locally optimal bundles. This paper therefore identifies that heuristic bundles can efficiently emerge by social evolutionary mechanisms whereby actors recursively exchange, adopt and perform bundles of rules constitute processes of heuristic emergence.

Such evolutionary emergence of socially calculated small bundles of heuristics differs from the agentic process by which some simple rule heuristics emerge or from the biological calculation process by which some behavioral biology heuristics emerge. The paper subsequently proceeds by classifying heuristics depending on their emergence process, distinguishing, on the one hand, agentic vs evolutionary mechanisms and, on the other hand, social vs biological encodings. The differences in the emergence processes of heuristics suggest the possibility of comparing them on three key characteristics – timescale, reflectivity and local optimality – which imply different forms of fitness.

The study proceeds as a conceptual elaboration; hence, it does not provide empirics. At a microlevel, it enables classification and comparison of the largest possible range of heuristics. At a macrolevel, it advocates for further exploration of managerial bundles of rules, regarding both their dynamics and their substantive nature.

In the field, practitioners are often observed to socially construct their theory of action, which emerges as a bundle of heuristics. This study demonstrates that such social calculations provide solutions that have comparatively good qualities as compared to heuristics emerging through other processes, such as agentic simple rules or instinctive – i.e. behavioral biology – heuristics. It should motivate further research on bundles of heuristics in management practice. Such an effort would improve the ability to produce knowledge fitting the absorptive capacity of practitioners and enhance the construction of normative managerial theories and pedagogy.

Bundles of rules may also play a crucial role in the emergence of collective action. This study contributes to a performativity perspective whereby theories can become reality. It demonstrates how the construction of a managerial belief system may amount to the launching of a social movement and vice versa.

Overall, many benefits accrue from integrating the bundles of rules expressed and exchanged by practitioners under the heuristic umbrella. So far, in management scholarship, such emergent objects have sometimes been interpreted as naïve or as indicative of institutional pressures. By contrast, this study shows that socially calculated bundles may efficiently combine the advantages of individuals' reflective cognitive processes with those provided by massive evolutionary exchanges. In conclusion, the social calculations of small heuristic bundles may constitute a crucial mechanism for the elaboration of pragmatic theories of action.

The way organizations cope with uncertainty in strategic decision making is prominently discussed. Concepts such as heuristics and simple rules are gaining increasing attention in strategic management research. However, despite their importance, little is known how heuristics and simple rules operate. Our qualitative study reveals that, first, strategic decisions consist of three basic elements: single rules, rule patterns, and emotional handling. Second, we find that firms develop generalizable rule patterns which follow a sequential order of inter-linked rules. Based on the findings we introduce the concept of organizational heuristics as inter-linked rule patterns drawing on organizational experience.

The purpose of this paper is to examine the profitability from various simple trading range break-out rules on the NASDAQ index.

Runs test is used to test whether the returns from every other days on buy and sell days are random. If they are not random, then the Student T-test will not be applicable to test the predictive power for profitability from the simple trading range break-out rules on the NASDAQ index.

Empirical study in this paper shows that the returns on buy and sell days are not random via runs test. Therefore, the simple trading range break-out rules cannot lead to the conclusion that they have the predictive power for profitability from the T-test. Applying the simple trading range break-out rule to NASDAQ does not support or overturn the market efficiency hypothesis.

The study is only based on the five simple trading range break-out rules from 9,311 daily closing prices on the NASDAQ over the period of February 5, 1971 to December 12, 2007. It can serve as a counter example for other studies about the predictive power of profitability from different trading rules.

Contrary to numerous previous research works, the study shows that the simple trading range break-out rules have no predictive power for profitability, and should not be used to test the market efficiency.

Based on the literature review, the study is one of the first empirical studies showing the returns on buy and sell days are not independent, and the authors cannot conclude that the trading range break-out rules have the predictive power for profitability on the NASDAQ index.

Expectations play a crucial role in finance, macroeconomics, monetary economics, and fiscal policy. In the last decade a rapidly increasing number of laboratory experiments have been performed to study individual expectation formation, the interactions of individual forecasting rules, and the aggregate macro behavior they co-create. The aim of this article is to provide a comprehensive literature survey on laboratory experiments on expectations in macroeconomics and finance. In particular, we discuss the extent to which expectations are rational or may be described by simple forecasting heuristics, at the individual as well as the aggregate level.

In this chapter, we explore the legal framework of AGMs in seven Member States (Austria, Belgium, Germany, France, Ireland, the Netherlands, and the United Kingdom) of shareholder decision-making rights. We find that, since only a small part of the decision-making rights is harmonized at the European level, there are numerous differences in shareholder rights among national laws. These decision-making rights are usually about the topics director (re-)elections, pay matters, share capital, amendments to articles of association, annual accounts, etc. To be able to conduct empirical research in the remaining chapters, we develop a categorization framework of 15 voting items.

Artificial Intelligence, Cybernetics, Production Describes the ELEKTRA reflective production rule interpreter, which differs from other interpreters by providing considerable support for meta‐level inference and reflection. Also describes the representations employed in the system, in which the control problem in production systems can be solved by increasing use of rules. Introduces the reflective properties of the system and gives examples. Shows that the interpreter on which the entire ELEKTRA system runs can be implemented as ELEKTRA rules.

Artificial‐life techniques – specifically, agent‐based models and evolutionary learning algorithms – provide a potentially powerful new approach to understanding some of the fundamental processes of war. This paper introduces a simple artificial‐like “toy model” of combat called Enhanced ISAAC Neural Simulation Tool (EINSTein). EINSTein is designed to illustrate how certain aspects of land combat can be viewed as self‐organized, emergent phenomena resulting from the dynamical web of interactions among notional combatants. EINSTein's bottom‐up, synthesist approach to the modeling of combat stands in stark contrast to the more traditional top‐down, or reductionist approach taken by conventional military models, and represents a step toward developing a complex systems theoretic toolbox for identifying, exploring, and possibly exploiting self‐organized emergent collective patterns of behavior on the real battlefield. A description of the model is provided, along with examples of emergent agent patterns and behaviors.

The purpose of this paper is to examine three different forms of returns based on the price difference, percentage change, and difference in logarithm price from moving average buy-sell trading rule. Statistical linear correlation, the means of returns from buy/sell days, and the flexibility of long-term moving periods are examined.

Traditional linear correlations, pairwise student t-test, and ϕ coefficient for two binary buy/sell decision variables are studied from the simple block bootstrap (convenience) sampling from S&P, Dow Jones, and NASDAQ price indices from January 29, 1985 to January 6, 2016.

The authors find that different forms of returns from MA(1-50) are strongly linearly correlated via 150 simple block bootstrap (convenience) samples from S&P, Dow Jones, and NASDAQ price indices from January 29, 1985 to January 6, 2016. In other words, the price differences, the percentage returns, and logarithmic returns are exchangeable for returns from S&P, Dow Jones, and NASDAQ. The authors refute the claims from Metghalchi et al.’s (2005, 2011) papers and Brock et al.’s (1992) paper. The authors conclude that the market is efficient and investors cannot gain benefits from moving average technical trading rule. Lastly, the authors find that the decisions from MA(1-50) and MA(1-200) are highly correlated; therefore, the length of periods used in long-period moving average is flexible.

It is one of the first studies about different forms of returns, their conclusions on the market efficiency, and the flexibility of long-term moving period for moving average buy/sell technical rules.

Discusses MCell (version 4.2), a popular cellular automata explorer for Windows™. Gives a brief overview of the program, demonstrates its capabilities and shows possible applications, presents the library of collected rules and patterns, and explains how user‐defined extensions can be programmed.