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A number of entropy models of social systems have been developed recently. Unfortunately, the complementarity of these approaches remains largely unanalysed, due to terminological and conceptual differences among them. There is an urgent need for a meta‐theoretical framework that will facilitate the analysis and comparison of all social entropy models. System entropy analysis (SEA), as presented here, is designed to fill this need. It is a second‐order, meta‐analytic tool which analyses each approach in terms of its major concepts, its basic units of analysis, its definition and measurement of entropy, and its specification of microstates and macrostates. First discusses the need for SEA, and then specifies its structure. Concludes with an application of SEA to the comparison and integration of three entropy approaches: synergetics, complexity theory and social entropy theory (SET).

Considers that when efficiency and effectiveness of systems are measured by the quantity and quality of their output they may be inadequate for the purpose of comparing one system with another. Seeks new criteria and defines entropy, wastivity and quality concepts in relation to system performance. Establishes the interdependence of the three concepts. Proposes that system wastivity should be used as a measure of system performance.

By combining the subjective probabilistic viewpoint of fuzziness with the entropy of deterministic functions, it is possible to expand an information theory of fuzzy sets which is fully compatible and consistent with the classical Shannonian information theoretic framework. A model of transinformation between fuzzy sets, which could be of help in approximate reasoning can be obtained, an interesting feature of which is that it can be duplicated in the framework of fuzzy set theory.

The paper attempts to address both resource consumption and recycling effectiveness, using concepts from thermodynamics: entropy production for evaluating the costs (resource consumption) and statistical entropy for evaluating the benefits (separation of materials) of recycling processes.

Resource consumption, in this context, is to be understood as the overall thermodynamic devaluation of matter and energy flows. The recycling effectiveness, on the other hand, can be measured by the process's ability to reduce the “mixedness” of the material flows, using statistical entropy (entropy of mixing) as an indicator. Statistical entropy has been used by others as an indicator for the performance of waste separation processes, and its application to metal recycling seems straightforward. Entropy production has been applied as a measure for resource consumption in copper production. Here, the two concepts are combined to reach an expression describing the resource efficiency of recycling.

The theoretical description of the approach is supported by an example calculation for copper recycling. The findings suggest a near perfect effectiveness of the copper separation when processing medium grade copper scrap in a primary copper smelter. The resource consumption, on the other hand, is quite large when compared to the service of the process, giving rise to a rather small thermodynamic efficiency (in terms of the definition of efficiency as applied in this paper).

Both measures used here, recycling efficiency and recycling effectiveness, are very demanding concerning the data basis, making applications time consuming. These drawbacks can be overcome by linking material flow tools (e.g. LCA software) with thermodynamic databases. More examples have to be considered to show the practical relevance of the approach.

The paper addresses effectiveness and efficiency using a common denominator, thermodynamic entropy. This unification helps in ranking different recycling options regarding their performance in terms of technical effectiveness and resource consumption.

Future developments of computer systems will be handicapped not by the limitations of hardware, but by our lack of understanding of the human reasoning processes. The development of three‐dimensional chips, cryogenic superconducting, or optical systems — and in due course, biological computers — presages the emergence of generations of super information processors whose power will dwarf the present generation of devices as they, in turn, have dwarfed the capacity of the computers of the pre‐transistor age. The effective application of such powerful future computers will be limited by the lack of an adequate theoretical basis for the processing of information. Gordon Scarrott has championed the need for a ‘science of information’ which should investigate the ‘natural properties of information such as function, structure, dynamic behaviour and statistical features…’ Such an effort should ‘… lead to a conceptual framework to guide systems design.’

In this article the mathematical formula of entropy is used to evaluate the degree of anarchy specific to multipolar, bipolar, and balance of power international political systems. Several entropic properties characteristic to these systems are deduced, and it is proved that alliances decrease international systems' entropy. Each international system it is observed is characterized by a specific amount of structural strain, and that under the assumptions of structural neo‐realism the entropy and structural strain move in opposite ways. On the basis of these findings a few properties regarding the stability of international systems are divided and the effects of alliances on international structural strain are examined. In the last part of the paper theoretical conclusions are tested against three empirical cases – the Cold War bipolar system, the XIXth Century European system, and the current one. The article ends with several methodological suggestions regarding a possible generalization of the entropic model.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

This paper aims to innovatively propose to improve the efficiency of satellite observation and avoid the waste of satellite resources, a genetic algorithm with entropy operator (GAE) of synthetic aperture radar (SAR) satellites’ task planning algorithm.

The GAE abbreviated as GAE introduces the entropy value of each orbit task into the fitness calculation of the genetic algorithm, which makes the orbit with higher entropy value more likely to be selected and participate in the remaining process of the genetic algorithm.

The simulation result shows that in a condition of the same calculate ability, 85% of the orbital revisit time is unchanged or decreased and 30% is significantly reduced by using the GAE compared with traditional task planning genetic algorithm, which indicates that the GAE can improve the efficiency of satellites’ task planning.

The GAE is an optimization of the traditional genetic algorithm. It combines entropy in thermodynamics with task planning problems. The algorithm considers the whole lifecycle of task planning and gets the desired results. It can greatly improve the efficiency of task planning in observation satellites and shorten the entire task execution time. Then, using the GAE to complete SAR satellites’ task planning is of great significance in reducing satellite operating costs and emergency rescue, which brings certain economic and social benefits.

The author examine the performance of a number of high short interest stocks along with the prices of the GameStop stock and three major stock exchange indices, particularly for the period after the eruption of the Covid-19 crisis.

With the employment of the complexity–entropy causality plane approach, the author categorize the stock prices in terms of the level of informational efficiency.

The author reported that the efficiency level for the index of the high short interest stocks falls considerably, not only at the onset of the Covid-19 crisis but during the health crisis period at hand. This is translated into proof of less uncertainty in predicting the stock prices of these specific stocks. On the other hand, the GameStop prices exhibit the same behavior as those with the high short interest firms, but change considerably in the middle of the crisis. The reversal of the behavior, by obtaining higher informational efficiency levels, is attributed to the short squeeze frenzy that increased the price of the stock many times over. Among the stock market indices, the Dow Jones Industrial Average and the S&P 500 decreased their efficiency levels marginally, after the surge of the crisis, while the Russell 2000 index kept the level intact. The high and stable degree of randomness could be attributed to the measures taken concurrently by the Federal Reserve and the government immediately after the outbreak of the crisis.

This is one of the few studies that examine the impact of short selling behavior on the efficiency level of certain stocks' prices, particularly during the health public crisis. It provides an alternative approach to measuring quantitatively the degree of inefficiency and randomness.

Entrepreneurs prioritise and act on purposeful endeavours instigated to actions by the visions of profits and benefits in the perceived opportunities. In the state of maximum entropy, with disorderliness and disequilibrium, entrepreneurs select the preferred pathway, through the profit-sensing mechanism, with the best probability of success to bet on. Therefore, this paper unpacks the forces at work in the mechanism to explain how entrepreneurs respond to opportunity and interpret the signals to coalesce into organised actions.

This research is primarily a conceptual paper on entrepreneurial action and the mechanism leading to that action. It refers to thermodynamic principles and biological cases to explain the forces at work using mostly analogical comparisons and similarities.

This paper aims to present an alternative theoretical scaffolding for entrepreneurship researchers to explore non-rational entrepreneurial behaviours and actions in uncertain, unstable and non-equilibrium environments, thereby creating new and competing hypotheses under the backdrop of adaptive evolution and thermodynamics phenomena.

The discussion featuring instinctively and naturally forming responses cannot fully explain the real entrepreneurial action as there is an element of free will and choices that are not discussed. While strategic choice and free-will shape decisions, they are preceded first by the attraction of the gradients and the biased motion in the direction of profit-attractant.

There remain essential links and issues not addressed in this “natural science”, constituting life science and physical science, oriented entrepreneurship research and exploration. Conceptualising opportunity-as-artefact and entrepreneurship as design, significant incidences of entrepreneurial actions can be explained by the presence of gradients stimulating entrepreneurial actions.

This viewpoint of information causality in opportunity-as-artefact casts a new look at the venerable question of what causes entrepreneurial actions. Shane and Venkataraman brought into focus this conversation, initiating the conceptual definition of opportunity. To have entrepreneurship, entrepreneurial opportunities must come first. Figuring the signals arising from these opportunities and cueing entrepreneurs to action is the main focus of this study.

Considering the “mechanism” at work and the thermodynamical forces at play, the entrepreneurial design process appears to hold considerable promise for future research development.