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Expands the idea that rational arbitrage not only may be limited in bringing about market efficiency but may actually generate price bubbles and make markets less efficient. It begins by presenting an alternative view of price patterns observed in the data on security returns—one based on feedback trading. It also describes the interactions of noise traders and arbitrageurs and shows that in cases in which arbitrageurs trade in anticipation of noise trader demand, they move the price away from rather than towards fundamental values.

This chapter examines Samuelson's Le Chatelier principle of how the market responds to a change in parameters of demand and supply curves. It uses examples in demand theory, economic policy, and empirical research to illustrate the principle. It also stresses the flexibility of the principle to adapt to changing assumptions. Changes from the optimizing agents to equilibrium systems whose primary use is to provide a foundation for understanding multipliers are observed. The chapter evaluates the performance of the principle performed when confronted with local optimization problems as in production function settings, and in positive feedbacks systems as in gaming situations. The principle is found progressive in that it is able to capitalize on symmetric relations among substitutes and complements. In that regard, the principle has extended research into multiplier analysis, a research area that will continue into the 21st century.

This chapter examines the main mechanisms leading to positive feedbacks, that is, self-reinforcement, such as imitative behavior and herding between investors. It shows that positive feedbacks contribute to the development of speculative bubbles, preparing the instability for a major crash. After providing an overview of feedbacks and self-organization in economics, the chapter explains how positive feedback on prices can arise due to hedging of derivatives and investment strategies with an “insurance portfolio.” It then describes a general mechanism for positive feedback, known as the “herd” or “crowd” effect, based on imitation processes. It also presents empirical evidence of herding by financial analysts and the relationship between “anti-imitation” and self-organization before concluding with an analysis of cooperative behaviors resulting from imitation, focusing on the Ising model of cooperative behavior and the complex evolutionary adaptive systems of boundedly rational agents.

Feedback is a twentieth-century name for a dynamic that has been understood to be basic to evolution at least since Darwin’s proposal of natural selection. Feedback is an important aspect of the so-called “modern evolutionary synthesis,” which guided thinking through much of the twentieth century and led especially to gene-centered views. And it has loomed particularly large in the “extended evolutionary synthesis” that has taken shape over the last thirty years and broadened such gene-centrism to take in other aspects of evolution. Feedback is paramount in coevolution, the processes by which organisms’ adaptational histories shift in relation to those of other organisms, both across different species and within the same species. Positive and negative feedback are each active in coevolution, but the effects of positive feedback are especially rich and, in some instances, unexpected. Standing outside both of these kinds of feedback is another aspect of evolutionary systems, one that has not received sufficient attention: feedforward, the impact of elements that influence or control feedback cycles but are not changed by them.

This chapter studies radiative, mechanical, and chemical feedback in the earliest gaseous clouds, taking up the thread of discussion in the previous chapter to consider the influences placed on the formation of second-generation stars. While the feedback effects are sufficiently complex that a complete description of them is well beyond the capabilities of present-day computer simulations, the general principles that underlie them are well known. Therefore, the chapter focuses on these principles and then briefly sketches the global picture. Feedback is important in all galaxies, and many of the principles that are discussed in this chapter apply on a much wider scale than just the first stars and galaxies.

This chapter lays the foundation for investigating system regimes and potential regime shifts (fundamental changes of ecosystem structure and feedbacks, with long-lasting effects on native biodiversity and ecosystem services) caused by biological invasions. System regime and bifurcation in dynamical systems, then early warning signals for detecting and predicting catastrophic regime shifts, especially the metrics associated with critical slowing down, are discussed. The concepts of bistability in both ecological and evolutionary systems and the regime shift related to jumping across basins of attraction are introduced. The chapter takes a fresh look at the concept of invasibility and considers the factors that can generate positive feedbacks, potentially leading to regime shifts through ecosystem engineering in invaded ecosystems.

The origins of complexity science lie in 50+ years of research into nonlinear dynamics in the fields of mathematics, physics, biology, information science, and system dynamics. Following numerous researchers who have argued for an inclusive definition of complexity, this chapter presents the entire scope of complexity science in terms of 15 fields. Each of these fields has its own theoretical frame, analytic methodology, and a set of applications in organization science and other social science disciplines. All of them offer a unique and nonlinear perspective for understanding complex dynamical systems. In Chapter 3, each field is summarized; they are deterministic chaos theory, catastrophe theory, fractals, positive feedback, power laws, system dynamics, complex adaptive systems, cellular automata, genetic algorithms, NK landscapes, agent-based modeling, autocatalysis, dissipative structures theory, ecosystem resilience, and evolutionary complexity.

My speculation in this paper is to consider, in short, what if art history is a system? In other words what does it means to think about art through the systems-thinking. To do so would mean understanding both art as a system and how art is also a part of other systems. It is my overall claim that to do so would require a rethinking of particular ideas about art and art history in ways that are both radical and effective. I begin by introducing some key feature of the systems-thinking approach. In short, systems thinking emerged in the mid 20th century along with related theories such as Cybernetics and Information Theory. Recently it expanded to incorporate the developments of 2^nd order cybernetics (Bateson) and dynamical systems theory (von Bertalanffy); examples of such developments include the Social Systems Theory of Niklas Luhmann and the use of systems by Bruno Latour and Gilles Deleuze. Whilst often very different these theories share an interest in: self-organizing systems; their behaviour and how they are defined by their interactions with their immediate environment. Systems-theory understands phenomena in terms of the systems of which they are part. A system is constituted by a number of interrelated elements that form a ‘whole’ different from the sum of its individual parts. When applied to art discourse it means considering not only works of art but also art museums, art markets, and art histories as systems that are autonomous, complex, distributed and self-organising. Examples of these types of speculations are offered. I conclude with two key speculations as to what the adoption of the systems-theoretical approach within art history might entail. Firstly, I argue that it is particularly effective in dealing with art after modernism, which is characterised by, amongst other things: non-visual qualities; unstable, or de-materialised physicality and an engagement (often politicised) with the institutional systems of support. By prioritising the systems of support over the individual work of art, or the agency of the individual artist such an approach is not tied by an umbilical cord of vision to an analysis based on traditional art historical categories such as medium, style and iconography. Secondly, I identify a tradition within art historical writing – Podro called it the Critical Historians of Art – that is known in the German tradition as Kunstwissenschaft (the systematic, or rigorous study of art.) I do so both as a means of clarifying what I mean when I say art history; but also as a means of identifying a tradition within art history of self-reflexivity and systematic investigation of methods and limits. From a systems-theoretical perspective it is an interesting question in its own right to ask why model of Kunstwissenschaft has become the dominant mode of historiography (since the 1980s at least). As a discourse it has become, in systems-theoretical terms, ‘locked-in’ (via positive feedback). It is my view that the systems theoretical approach to art discourse places it within the art historical tradition of Kunstwissenschaft, and is not in opposition to it. In summary, it is not my intention to either attack or defend a straw-man, or flimsy stereotype of what art history is. I am rather, seeking a body of work, a canon, or discursive system, with which to engage. Overall my claim is that the systems theoretical approach to art discourse is a continuation of this rich and worthy heritage (of finding historical models to match the art under scrutiny)—not a break from it.

Chapter 2 provides an overview of the neural and endocrine mechanisms that govern the timing and onset of puberty (reproductive maturation). The cells and hormones that comprise the hypothalamic–pituitary–gonadal (HPG) axis are introduced, followed by an explanation of how both negative and positive neuroendocrine feedback loops regulate circulating levels of gonadal steroid hormones in males and females. The rest of the chapter is devoted to mechanisms that govern the timing of puberty and activation of the HPG axis at the onset of puberty. The role of the metabolic hormone leptin as a permissive signal for the timing of puberty, the role of neural excitation and disinhibition in the awakening of the gonadotropin-releasing hormone (GnRH) neurons at the onset of puberty, and the role of the neuropeptide kisspeptin as a proximal driver of HPG axis activation are highlighted. Finally, recent research on hierarchical gene networks that are ultimately responsible for the developmental unfolding of activation of GnRH neurons at puberty onset is reviewed.

Judith Harackiewicz reflects on her most underappreciated work: a paper entitled “Rewarding pinball wizardry” that explored the effects of rewards on intrinsic motivation. Harackiewicz performed a pinball experiment with her graduate students to test the idea that rewards for competence might have negative effects due to the stress of performance evaluation, but that they might also have positive effects because they provide positive feedback upon receipt.

Are behavioral biases prevalent in commodities and futures markets? Although retail equity investors display many psychological biases, investors who are more sophisticated exhibit fewer biases. The market makers, traders (locals), speculators, hedgers, and institutions of the commodities and futures markets tend to be professional participants, and thus less prone to behavioral biases. Nevertheless, the fast-paced action of these markets is an environment that fosters behavioral errors. This chapter reviews the literature on the pervasiveness of prospect theory behavior and other biases in these markets. Strong evidence indicates that market participants exhibit loss aversion, the impact of reference points, the disposition effect, and overconfidence. They also engage in positive feedback trading and momentum investing. Lastly, the chapter reviews risk-taking and behavioral biases by the type of market participant, particularly focusing on market makers, floor traders, clearing members, and the public.

This chapter starts with proving the inevitability of population growth regulation, and concludes with an explanation of the exclusive resource limitation principle that, through determining community structure, shapes the landscape surrounding us. Population regulation may be mediated by resource-limitation (Tilman model) or site-limitation (Levins model), or natural enemies like predators, parasites, or parasitoids. Food chain length sometimes determines the main quantitative features of complete communities through top-down regulation. All regulatory mechanisms share one feature: they feed back population abundance on population growth, ultimately setting strict limits on population growth and fluctuations, even if facilitation-induced positive ecological feedbacks (e.g. Allee effects) may act at low population sizes. The way of modelling interactions between individuals (e.g., functional responses) is explained and illustrated by examples. The relations of explicit (logistic) and implicit (process-based) models of population dynamics and some model-based interpretations of case studies and experimental results are shown.

Two models of market behavior are contrasted, one characterized by negative feedbacks, where the agent's relationship to the commodities being exchanged is determined independently of the market, the other by positive feedbacks, where the same relationship is no longer assumed to be determined in advance of transactions. The fundamentalist model, corresponding to the Walrasian framework, is assumed by neoclassical economists to be ideally suited to analyzing financial markets. This is shown to be mistaken, and their inability to explain financial competition shown to be due to a misunderstanding of liquidity. The main properties of liquidity are examined instead in relation to the self-referential model, which provides a simple explanation of excessive volatility and speculative bubbles. Speculation is analyzed as the product of emergent and collective perceptions known as conventions. The behavior of governments and markets is conventional since both regard liquidity as the supreme arbiter of economic exchange.

This chapter describes the concept of fractals and their self-similarity, including fractals with complex dimensions. It shows how these geometric and mathematical objects enable one to codify the information contained in the precursory patterns before large stock market crashes. The chapter first considers how models of cooperative behaviors resulting from imitation between agents organized within a hierarchical structure exhibit the announced critical phenomena decorated with “log-periodicity.” It then examines the underlying hierarchical structure of social networks, critical behavior in hierarchical networks, a hierarchical model of financial bubbles, and discrete scale invariance. It also discusses a technique, called the “renormalization group,” and a simple model exhibiting a finite-time singularity due to a positive feedback induced by trend following investment strategies. Finally, it looks at scenarios leading to discrete scale invariance and log-periodicity.

Chapter eight analyzes why institutions persist, even when they generate unintended consequences for the states that created them. The chapter sets out a typology of possible actions that governments can take to exit from investor–state arbitration. To date, governments have engaged in remarkably little exit. The second section explores how positive feedback has created a new constituency of law firms and investors with an interest in arbitration and therefore has led to a new politics of ISDS. The third section discusses other types of feedback that have stabilized and developed a dense web of commitments enshrining investor–state arbitration. The fourth section observes that over time, competitive dynamics emerged and define investor–state arbitration today: competition between law firms, arbitration organizations, and even jurisdictions hoping to host arbitrations makes exit and reform more difficult. The barriers to exit may be highest for capacity-constrained states.

This chapter examines stock market crashes in the entire financial history of the United States as well as the world economy and population dynamics over the last 2,000 years. It suggests the existence of strong positive feedbacks that point to an underlying finite-time singularity around 2050, signaling a fundamental change of regime of the world economy and population around 2050 (a super crash?). Three leading scenarios are described: collapse, transition to sustainability, and superhumans. After analyzing financial as well as economic and population times series over the longest time scales for which reliable data is available, the chapter considers the pessimistic viewpoint of “natural” scientists vs. the optimistic viewpoint of “social” scientists regarding human population size and growth. It also discusses the faster-than-exponential growth of population, GDP, and financial indices before concluding with an overview of the increasing propensity to emulate the stock market approach.

This chapter builds a new explanation for the rise of ISDS, drawing on the theoretical tradition of historical institutionalism. It begins with a general discussion of the promise of historical institutionalism, focusing on preferences and unintended consequences. The second section introduces the concept of critical juncture and argues that international officials can determine the outcome of a critical juncture, if certain conditions are met. The chapter specifies these conditions as well as motivations, resources, and strategies of international officials. The third section turns to gradual institutional development, and introduces feedback effects, layering, and conversion as mechanisms that help us understand how institutional structure can be reproduced while simultaneously entailing novel institutional purposes and unintended consequences.

This chapter reviews the general principles that are necessary for a neural system to make decisions. A glance at the literature shows that the simplest system to obtain an imbalance between two populations of neurons subjected to the same activation consists of two interconnected populations of inhibitory neurons. These two populations exert lateral inhibition on each other. In order for a differential response to emerge, noise is necessary. Synaptic noise is considered the main source of noise in the nervous system. The chapter then goes on to look at positive feedback. It also studies the learning processes in the nervous system and explores neural plasticity rules, particularly the Hebbian learning rule.

The diversity of life forms depends on the existence of the eukaryotic cell. Eukaryotes overcome scaling conflicts where increased size separates the working structural parts from genetic instructions that lets them play their role. For engineers emergence means collapse and negative, unfortunate outcomes. Accordingly engineers overbuild. In ecological systems complexity is a way around emergence in that it expects radical change and works to incorporate it as a normal happening. Biological and ecological systems work with varying contexts that change inertial frames. The chapter presents a protocol for using scale. Three alternatives are: 1. use an easily applied scale; use an elaborate exact scale. 2. change the scale; change the point of view. 3. focus on the form of the scale used; focus on what appears using the scale. It appears that society is at its scaling limits, about to be overcome. Complexity in both the situation and in the response causes social problem solving itself to cease to work. Issues of scale and complexity appear to underlie the disenchantment in the First World. It may take us down as it did the Romans.