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Certain elements of Hayek’s work are prominent precursors to the modern field of complex adaptive systems, including his ideas on spontaneous order, his focus on market processes, his contrast between designing and gardening, and his own framing of complex systems. Conceptually, he was well ahead of his time, prescient in his formulation of novel ways to think about economies and societies. Technically, the fact that he did not mathematically formalize most of the notions he developed makes his insights hard to incorporate unambiguously into models. However, because so much of his work is divorced from the simplistic models proffered by early mathematical economics, it stands as fertile ground for complex systems researchers today. I suggest that Austrian economists can create a progressive research program by building models of these Hayekian ideas, and thereby gain traction within the economics profession. Instead of mathematical models the suite of techniques and tools known as agent-based computing seems particularly well-suited to addressing traditional Austrian topics like money, business cycles, coordination, market processes, and so on, while staying faithful to the methodological individualism and bottom-up perspective that underpin the entire school of thought.

Compares the philosophical backgrounds of the disciplines of economics and accounting in view of complexity theory. The relationship which has existed between the two is examined as well as the problems of such inter‐disciplinary studies. Decision making, target costing and the need for future collaboration are discussed in light of the theory.

The term “agent-based modelling” (ABM) is a buzzword which is widely used in the scientific literature even though it refers to a variety of methodologies implemented in different disciplinary contexts. The numerous works dealing with ABM require a clarification to better understand the lines of thinking paved by this approach in economics. All modelling tasks are a means and a source of knowledge, and this epistemic function can vary depending on the methodology. this paper is to present four major ways (deductive, abductive, metaphorical and phenomenological) of implementing an agent-based framework to describe economic systems. ABM generates numerous debates in economics and opens the room for epistemological questions about the micro-foundations of macroeconomics; before dealing with this issue, the purpose of this paper is to identify the kind of ABM the author can find in economics.

The profusion of works dealing with ABM requires a clarification to understand better the lines of thinking paved by this approach in economics. This paper offers a conceptual classification outlining the major trends of ABM in economics.

There are four categories of ABM in economics.

This paper suggests a methodological categorization of ABM works in economics.

The objective of this paper is to show the modernity of the approach developed by Carl Menger. The author argues that the Mengerian approach is part of a conceptual pattern composed of four interdependent, hierarchical and coherent parts (ontology, epistemology, methodology and key concepts) and that this conceptual pattern in all its originality and consistency fits perfectly within the approach of complexity. Ontologically (Section 1), the starting point is different from that of Léon Walras and William Stanley Jevons, the economy being apprehended as an open system; from an epistemological point of view (Section 2), Menger adopts a distinct conception of what constitutes a good scientific explanation, which contrasts with the Walrasian conception; methodologically (Section 3), his rejection of mathematics can therefore be understood as a consequence of his ontological and epistemological position: it is not mathematics as such that the author rejects but the functionalist tools then available, which are not adapted to his conception of economic reality; and finally the key concept (Section 4) of the author’s analysis is not that of equilibrium but an analysis of the process of exchange and production, with the emphasis placed on the emergence of organic phenomena.

This paper aims to investigate the role of predictive models in the learning and decision-making processes of strategic management. The rapid advancement of digitalisation has contributed to increasing the complexity of the worldwide economy and led to various new competitive dynamics.

To achieve this purpose, a literature review has been carried out and a predictive model based on Watson, an IBM supercomputer, is presented as a qualitative process model.

Specific insights derived from a review of the literature highlight organisations' need to modify their decision- and strategy-making processes, which are increasing in speed and frequency, thus also leading to the formulation of emergent and trigger event strategies based on the identification of conditions that require the revision of all or part of the firm's strategy. Predictive models, acting as filters, transform data into informative knowledge that decision-makers can interpret based on individual domain knowledge.

From a theoretical point of view, this paper contributes to the field of digital transformation by proposing the economics of complexity as a paradigm through which to observe and study the issue of predictive models in strategic management. Additionally, the authors analyse the phenomenon from a cognitive perspective, defining the new learning dynamics of digital transformation and the social learning cycle triggered by big data and predictive models. From a managerial and policy-making point of view, this suggests the need to re-shape traditional education contents and dynamics and foster skills that are multi-disciplinary, multi-domain, multi-empathic, multi-interaction and multi-communication between people and things.

Scholars have examined, in various ways, the complexity of knowledge in innovation. Recently, research has begun to focus on the role of a continuous process of knowledge recombination in our understanding of a changing structure of knowledge complexity and knowledge accumulation. Furthermore, we also claim that this process may reflect changes in the underlying innovation paradigm, or in other words the arrival of the information age. Yet, little is known about how knowledge complexity is increasing in the broader context of globalization, in which the influence of a rising diversity of locational sources may feature more prominently. We consider how knowledge recombination that relies upon the global spread of innovation activities will affect our theory of the relationship through which earlier contributions to knowledge become inputs to subsequent knowledge building that generates more (or less) complex knowledge artifacts. We propose that knowledge complexity rises when recombined elements are sourced across two dimensions of distance simultaneously, namely when sources which are derived from (i) disparate knowledge fields and (ii) distinct geographic locations are combined. We thereby develop an international business perspective on knowledge complexity through recombination by better appreciating the processes that may be necessary when knowledge is combined along global value chains. We also suggest some implications for changing organizational forms by highlighting the value of connecting previously unconnected geographically distant elements, which suggests a greater potential for more informal and indirectly diffused knowledge-based connections.

The purpose of this exploratory paper is to examine the efficient market theories and to argue that a new paradigm or an expanded paradigm is needed for the valuation of real estate. This may actually not be a new paradigm but it may be necessary to go back in time to make the valuation models that are used more realistic and to try to include the realities that there are many diverse actors in the real estate marketplace and their actions are important and should not be assumed away. Behavior matters and the models for pricing real estate need to take this into account.

The paper examines some of the emerging models in other disciplines and works to relate them to the real estate marketplace in general but, more importantly, to help to explain the most recent bust of the global real estate markets.

The paper finds that there is a need to consider an alternative paradigm for the valuation of real estate and complexity theory as well as the adaptive system models that specifically take into account that the various actors in a real estate marketplace could be used to help better explain the emergent nature of real estate values.

This is the first paper to one's knowledge that argues for a shift in thinking to include complexity economics and agent‐based modeling as potential solutions to gain a better understanding of how real estate markets react.

This article aims to evaluate the entry and exit of companies from local productive structures, with a specific focus on the sectoral complexity of these activities and the complexity of these portfolios. The study focuses on empirically demonstrating the thesis that related economic diversification exacerbates the development gap between more and less complex regions.

The article uses indicators formulated by the economic complexity approach. They allow a relevant descriptive analysis of the economic diversification process in Brazilian micro-regions and provide the foundation for the econometric tests conducted. Through three distinct estimation strategies (OLS, logit, probit), the influence of complexity and relatedness on the entry and exit events of firms from local portfolios is tested.

In all estimated models, the stronger relationship between an activity and a portfolio significantly increases its probability of entering the productive structure and, at the same time, acts as a significant factor in preventing its exit. Furthermore, the results reveal that the complexity of a sector reduces the probability of its specialization in less complex regions while increasing it in more complex regions. On the other hand, sectoral complexity significantly increases the probability of a sector leaving less complex local structures but has no significant effect in highly complex regions.

Due to the data used, the indicators are calculated considering only formal job numbers. Additionally, the tests do not detect the influence of spatial issues. These limitations should be addressed by future research.

The article characterizes a prevailing process of uneven development among Brazilian regions and brings relevant implications, primarily for policymakers. Specifically, for less complex regions, policies should focus on creating opportunities to improve their diversification capabilities in complex sectors that are not too distant from their portfolios.

The article makes an original contribution by proposing an evaluation of regional diversification in Brazil with a focus on complexity, introducing a more detailed differentiation of regions based on their complexity levels and examining the impact of sectoral complexity on diversification patterns within each group.

The purpose of this exploratory paper is to examine the lack of reliability of traditional neo-classical models and to argue that it is due to the hidden complexity and non-linearity that may operate at times in residential housing markets. As a result, market efficiency may be a special case, rather than the prevailing rule. An alternative framework that incorporates the higher order concepts of complexity – based on the non-linear, emergent behavior of multiple agents – is required to model discontinuities and imbalances in the housing markets.

The paper examines the building block concepts required to model the complexity of the housing market and analyzes their implications. These implications can be counter-intuitive and help explain the failure of policy makers to model the recent bust in global housing markets.

The paper finds that policy makers need to adopt an analytical framework that incorporates non-linearity, emergence and other building blocks of complexity in order to construct representative financial models that help understand systemic imbalances that may afflict residential housing markets.

This is the first paper to one's knowledge that argues that policy makers should adopt an alternative theoretical framework based on complexity concepts in order to create more effective financial models; such models should include indicators that provide early warning signals of potential discontinuities in housing markets.

The economics literature on gender has expanded considerably in recent years, fueled in part by new sources of data, including from experimental studies of gender differences in preferences and other traits. At the same time, economists have been developing more realistic models of psychological and social influences on individual choices and the evolution of culture and social norms. Despite these innovations, much of the economics of gender has been left behind, and still employs a reductive framing in which gender gaps in economic outcomes are either due to discrimination or to “choice.” I suggest here that the persistence of this approach is due to several distinctive economic habits of mind – strong priors driven by market bias and gender essentialism, a perspective that views the default economic agent as male, and an oft-noted tendency to avoid complex problems in favor of those that can be modeled simply. I also suggest some paths forward.