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Most insights of environmental economics are in line with the standard neo-classical economic model of rational behaviour, formulated in terms of maximisation of utility in general, or profits in particular. The standard theory of environmental policy is a case in point. However, the maximisation hypothesis and its methodological foundation have been criticised on many grounds, related to a lack of either logical or empirical content. Moreover, over the years great many alternative models of behaviour have been proposed. Both criticism and alternatives are surveyed here. In the context of environmental economics behavioural assumptions have been most significant for the development of economic valuation theory and environmental policy theory. The focus here will be on environmental policy theory.

We discuss the literatures on behavioral economics, bounded rationality and experimental economics as they apply to firm behaviour in markets. Topics discussed include the impact of imitative and satisficing behavior by firms, outcomes when managers care about their position relative to peers, the benefits of employing managers whose objective diverges from profit-maximization (including managers who are overconfident or base pricing decisions on sunk costs), the impact of social preferences on the ability to collude, and the incentive for profit-maximizing firms to mimic irrational behavior.

Today, diagnostic reasoning combines common and specialized knowledge, elements of numeracy, some facility with the basics of probability theory and, last but not least, ease in interactions with AI tools. We present procedures and tools for constructing trees that lead to understandable, transparent, simple, and robust classifications and decisions. These tools are more heuristic than optimal models, inspired by the perspective of Bounded Rationality. We describe how the tenets of Bounded Rationality provide a framework for the human-machine interaction this paper is devoted to. We claim that, because of this rationality, our proposed tools facilitate machine-aided decision making that is smooth, transparent and successful.

pt. 1. Role theory : the puzzle of Britain's appeasement decisions in the 1930s -- pt. 2. Role demands : substantive rationality and structural adaptation -- pt. 3. Role conceptions : bounded rationality and experiential learning -- pt. 4. Role enactments : communicative rationality and altercasting.

Governments and its officials today face increasing pressure to innovate. But often the reforms and policies proposed are formulated under conditions of limited knowledge. This bounded rationality may foster policy innovation, but in many cases public officials instead may seek to learn from other jurisdictions in the formulation of their policy options. This paper examines how governments learn, innovate, and make decisions. Using the United States as an example, this article contends: 1) There is often a significant gap between social science and scientific knowledge and the information governments use in making policy; 2) That in many cases public officials lack the capacity to digest appropriate information when making policy; and 3) That government decision making under the conditions of bounded rationality often produces less innovation and more similarly in terms of policy responses. Overall, the article will generalize from the experience of the United States to indicate the implications for other nation states as they seek to formulate policies and learn from one another in global politicaleconomic system. ; В настоящее время правительства и чиновники сталкиваются с более высокими требованиями к инновациям. Но часто реформы и направления политики формируются в условиях ограниченного знания. Такая ограниченная рациональность может способствовать развитию инновационной политики, но вместо названного подхода во многих случаях государственные чиновники стремятся освоить другие области компетенции для формулирования проводимой ими политики. В данном исследовании рассматриваются вопросы, касающиеся того, как правительство изучает, создает инновации и принимает решения. Используя США в качестве примера, в данной статье сделаны выводы о том, что: 1) часто существует значительный разрыв между социальной наукой и научным знанием и информацией, которую правительство использует при принятии государственных решений; 2) во многих случаях государственные должностные лица не имеют возможности использовать и анализировать соответствующую информацию при проведении государственной политики; 3) правительство принимает решения в условиях ограниченной рациональности, зачастую осуществляет меньше инноваций и чаще использует «готовые решения» при осуществлении политики. В статье обобщен опыт Соединенных Штатов Америки, чтобы указать на последствия для других государств при формулировании собственной политики с учетом обмена опытом в условиях глобальной политико-экономической системы.

AbstractWe consider price and capacity decisions for a profit‐maximizing service provider in a single server queueing system, in which customers are boundedly rational and decide whether to join the service according to a multinomial logit model. We find two potential price‐capacity pair solutions for the first‐order condition of the profit‐maximizing problem. Profit is maximized at the solution with a larger capacity, but minimized at the smaller one. We then consider a dynamically adjusting capacity system to mimic a real‐life situation and find that the maximum can be reached only when the initial service rate is larger than a certain threshold; otherwise, the system capacity and demand shrink to zero. We also find that a higher level of customers' bounded rationality does not necessarily benefit a firm, nor does it necessarily allow service to be sustained. We extend our analysis to a setting in which customers' bounded rationality level is related to historical demand and find that such a setting makes service easier to sustain. Finally we find that bounded rationality always harms social welfare.

My Ph.D. thesis contributes to the growing literature investigating the role of heterogeneity in the macroeconomics volatility. Particularly, it focuses on bounded rationality and heterogeneous expectations in financial accelerator frameworks. In the first paper, I present a literature review tackling the theoretical and the empirical challenges of bounded rationality and heterogeneous expectations. More precisely, I decided to analyse the Adaptive Belief System (ABS) because it is relevant to the purpose of solving the "wilderness problem" of bounded rationality. Moreover, it introduces complex dynamical evolution in the system and it can describe many stylized financial and macroeconomics phenomena, like fat tails, unpredictable returns and excess volatility. I conclude the survey showing some experimental analyses suggesting the importance of heterogeneity and bounded rationality in the expectations formation and in the evolution of the economic system. In the other two papers, I elaborate two financial accelerator (FA) frameworks focusing on the role of heterogeneous expectations, investment decisions and macroeconomic fluctuations. In the first one, starting from the Bernanke-Gertler-Gilchrist financial accelerator (1999), I develop an Agent-Based financial accelerator introducing bounded rationality, heterogeneous expectations, actual bankruptcy and a balance sheet for the financial intermediary. I specify a setup in which the heterogeneity is inserted in the agents' wealth as well as in their heuristics. The agents make mistakes in forecasting future macroeconomic variables and update their beliefs when new information becomes available. Since they commit mistakes in their investment and borrowing decisions, the entrepreneurs may not be able to fulfil their debt and therefore go bankrupt. To account for the losses of defaults I introduce the balance sheet of the financial intermediary. Then, the bankruptcy affects the credit channel: firstly because the bank will settle an extra cost to the defaulted entrepreneur, secondly because banks with lower financial soundness will fix on average higher interest rate on loans. Finally, I explore the macroeconomic volatility running some simulations which consider different heuristics and different monetary policies. My results suggest that a monetary authority should take into account the "sentiment of the market" when designing its policy. Indeed, applying the same monetary policy may have different consequences on macroeconomic volatility when the expectations are non-identical. However, if two monetary policies are implemented in the same scenario, it seems that the strongest monetary policy will reduce the waves of optimism and pessimism better stabilizing the macroeconomic environment. In the second model, I propose a financial accelerator in which the evolution of expectations is based on the adaptive belief system. Within this framework, the entrepreneurs have cognitive limitations and are not able to forecast in advance the actual return on capital. However, when new information becomes available, they can compute their investment performance and switch to the most performing heuristic. Through this mechanism, they update their beliefs on future investment return introducing complex dynamics in the model. In the last part of the paper, I explore the macroeconomic volatility of the system considering different heuristics and monetary policies. One the one side, the core results suggest that no monetary policy is able to quickly stabilize the system completely; some fluctuations persist for many periods. Moreover, flexible inflation targeting policies yield lower fluctuations but these are more persistent. On the contrary, strict inflation targeting policies produce deeper macro-volatility but lower persistency. Finally, the stabilizing effect of the monetary policy strongly depends on the nature of the heuristics and, counterintuitively, the volatility is higher in scenarios with more sophisticated heuristics.

Peter Albin is known for his seminal work in applying the concepts of adaptive dynamical systems, first developed by biologists and physicists, to the study of economic systems. This book is a collection of his pathbreaking articles on the application of cellular automata and complexity theory to economic problems. Duncan Foley provides a thoughtful introduction in which he reviews the disparate analytical sources of Albin's work in the theories of nonlinear dynamical systems, economic dynamics, cellular automata, linguistic and computational complexity, and bounded rationality. Albin has analyzed economic systems as interactions of highly complex components (i.e., intelligent human beings). He uses the theories of generative linguistics and cellular automata to establish that the complexity level of economic systems is, in principle at least, that of a Turing machine or general-purpose computer, establishing that classic economic approaches to the problems of household and firm choice, macroeconomic prediction, and policy evaluation may give rise to undecidable propositions and uncomputable functions. He develops simple models of dynamic economic interaction based on cellular automata which illustrate the inherent complexity of economic interactions and the resulting challenge they pose to traditional theories of rational economic behavior. These models explore the dynamics of the business cycle, decentralized market trading, and the emergence of cooperation in a novel local-interaction version of the repeated prisoners' dilemma game. Albin's work provides a unique and important perspective on economic systems

Since the conceptualization of bounded rationality by Herbert Simon (1947), management scholars started investigating how people—managers and entrepreneurs—really make decisions within (and for) organizations [...]