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Neuroeconomics and modern neuroscience

CEE-M Working Papers from CEE-M, Universtiy of Montpellier, CNRS, INRA, Montpellier SupAgro

Abstract: The paper is an overview of the main significant advances in the knowledge of brain functioning by modern neuroscience that have contributed to the emergence of neuroeconomics and its rise over the past two decades. These advances are grouped over three non-independent topics referred to as the “emo-rational” brain, “social” brain, and “computational” brain. For each topic, it emphasizes findings considered as critical to the birth and development of neuroeconomics while highlighting some of prominent questions about which knowledge should be improved by future research. In parallel, it shows that the boundaries between neuroeconomics and several recent subfields of cognitive neuroscience, such as affective, social, and more generally, decision neuroscience, are rather porous. It suggests that a greater autonomy of neuroeconomics should perhaps come from the development of studies about more economic policy-oriented concerns. In order to make the paper accessible to a large audience the various neuroscientific notions used are defined and briefly explained. In the same way, for economists not specialized in experimental and behavioral economics, the definition of the main economic models referred to in the text is recalled.

Neuroeconomics is still a nascent scientific field, two decades old at the most. Although much remains to be done, a great deal of results has already been proven about how the human brain makes choices, and these findings provide insights into the understanding of economic behavior in many domains. Undoubtedly, without the availability of an extensive variety of experimental designs for dealing with individual and social decision-making provided by experimental economics, many neuroeconomics studies could not have been developed. Indeed, it is very likely that, for future historians of economics, lab experiments will be “one of the most stunning methodological revolutions in the history of science” (Guala, 2009, 152). At the same time, without the significant progress made in modern neurosciencefor grasping and understanding brain functioning, neuroeconomics would have never seen the light of day.

Keywords: neuroeconomicsneurosciencebehavioral economicsexperimental economics 
Date: 2019

Daniel Serra
Additional contact information

Downloads: (external link) 


Gamification, a new form of human-centred design?

Just like HCD a decade ago, gamification is an emerging and evolving discipline. Questions being asked by practitioners and researchers include the benefits of different mechanics and design steps, the long-term effects of gamified experiences, the ethics of gamification, and more. Just like HCD, gamification requires practitioners and designers to lead with desirability instead of viability and use the power of design for good, not evil. This is especially important because the inherent purpose of gamification is to provide an engaging experience to nudge behaviour.

Gamification as a branch of HCD continues to evolve and mature and just like HCD a decade ago, it requires application, inquiry and refinement to deliver on its purpose of engaging users to solve real world problems.


Why Did Pre-Modern States Adopt Big-God Religions?

Abstract: Over the past two millennia successful pre-modern states in Eurasia adopted and cultivated Big-God religions that emphasize (i) the ruler’s legitimacy as divinely ordained and (ii) a morality adapted for large-scale societies that can have positive economic effects. We make sense of this development by building on previous research that has conceptualized pre-modern states as maximizing the ruler’s profit. We model the interaction of rulers and subjects who have both material and psychological payoffs, the latter emanating from religious identity. Overall, religion reduces the cost of controlling subjects through the threat of violence, increases production, increases tax revenue, and reduces banditry. A Big-God ruler, who is also a believer, has greater incentives to invest in expanding the number of believers and the intensity of belief, as well as investing in state capacity. Furthermore, such investments are often complementary, mutually reinforcing one another, thus leading to an evolutionary advantage of rulers that adopted Big-God religions.

Keywords: StateRulerAnarchyReligionMoralityLegitimacyState capacity (search for similar items in EconPapers)
JEL-codes: D70 H0 N40 P40 Z1 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-evo and nep-his
Date: 2019-06
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (application/pdf)

Stergios Skaperdas ( and Samarth Vaidya (
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No 181908, Working Papers from University of California-Irvine, Department of Economics



La legge multiscala della varietà richiesta è una legge scientifica relativa, in ciascuna scala, alla variazione di un ambiente rispetto alla variazione dello stato interno necessaria per una risposta efficace da parte di un sistema. Sebbene questa legge sia stata utilizzata per descrivere l’efficacia dei sistemi nell’autoregolamentazione, le conseguenze per il fallimento non sono state formalizzate. Qui usiamo questa legge per considerare le dinamiche interne di un sistema non strutturato e la sua risposta ad un ambiente strutturato. Scopriamo che, a causa della sua incapacità di rispondere, un sistema completamente non strutturato è intrinsecamente instabile per la formazione della struttura. E in generale, qualsiasi sistema senza una struttura al di sopra di una certa scala non è in grado di resistere a una struttura che sorge sopra quella scala. Per descrivere complicate dinamiche interne, sviluppiamo una caratterizzazione di modifiche multiscala in un sistema. Questa caratterizzazione è motivata dalle idee teoriche del rumore di Shannon, ma considera le informazioni strutturate. Quindi colleghiamo le nostre scoperte all’anarchismo politico mostrando che la società richiede alcuni processi organizzativi, anche se non esiste un governo o gerarchie tradizionali. Formuliamo anche i nostri risultati come una seconda legge inversa della termodinamica; mentre i sistemi chiusi collassano in disordine, i sistemi aperti a un ambiente strutturato generano spontaneamente ordine.



The Multiscale Law of Requisite Variety is a scientific law relating, at each scale, the variation in an environment to the variation in internal state that is necessary for effective response by a system. While this law has been used to describe the effectiveness of systems in self-regulation, the consequences for failure have not been formalized. Here we use this law to consider the internal dynamics of an unstructured system, and its response to a structured environment. We find that, due to its inability to respond, a completely unstructured system is inherently unstable to the formation of structure. And in general, any system without structure above a certain scale is unable to withstand structure arising above that scale. To describe complicated internal dynamics, we develop a characterization of multiscale changes in a system. This characterization is motivated by Shannon information theoretic ideas of noise, but considers structured information. We then relate our findings to political anarchism showing that society requires some organizing processes, even if there is no traditional government or hierarchies. We also formulate our findings as an inverse second law of thermodynamics; while closed systems collapse into disorder, systems open to a structured environment spontaneously generate order.


Semantic information, agency, & physics

via Semantic information, agency, & physics

Shannon information theory provides various measures of so-called syntactic information, which reflect the amount of statistical correlation between systems. By contrast, the concept of ‘semantic information’ refers to those correlations which carry significance or ‘meaning’ for a given system. Semantic information plays an important role in many fields, including biology, cognitive science and philosophy, and there has been a long-standing interest in formulating a broadly applicable and formal theory of semantic information. In this paper, we introduce such a theory. We define semantic information as the syntactic information that a physical system has about its environment which is causally necessary for the system to maintain its own existence. ‘Causal necessity’ is defined in terms of counter-factual interventions which scramble correlations between the system and its environment, while ‘maintaining existence’ is defined in terms of the system’s ability to keep itself in a low entropy state. We also use recent results in non-equilibrium statistical physics to analyse semantic information from a thermodynamic point of view. Our framework is grounded in the intrinsic dynamics of a system coupled to an environment, and is applicable to any physical system, living or otherwise. It leads to formal definitions of several concepts that have been intuitively understood to be related to semantic information, including ‘value of information’, ‘semantic content’ and ‘agency’.


Hello World: Being Human in the Age of Algorithms (Hannah Fry)

via Hello World: Being Human in the Age of Algorithms (Hannah Fry)

A look inside the algorithms that are shaping our lives and the dilemmas they bring with them.

If you were accused of a crime, who would you rather decide your sentence―a mathematically consistent algorithm incapable of empathy or a compassionate human judge prone to bias and error? What if you want to buy a driverless car and must choose between one programmed to save as many lives as possible and another that prioritizes the lives of its own passengers? And would you agree to share your family’s full medical history if you were told that it would help researchers find a cure for cancer?


Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network

via Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network

Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network
Link prediction is one of the fundamental research problems in network analysis. Intuitively, it involves identifying the edges that are most likely to be added to a given network, or the edges that appear to be missing from the network when in fact they are present. Various algorithms have been proposed to solve this problem over the past decades. For all their benefits, such algorithms raise serious privacy concerns, as they could be used to expose a connection between two individuals who wish to keep their relationship private. With this in mind, we investigate the ability of such individuals to evade link prediction algorithms.

Time is real? I think not

maggio: 2020

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