Archivio per luglio 2019

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Simulating Acculturation Dynamics Between Migrants and Locals in Relation to Network Formation

Abstract

International migration implies the coexistence of different ethnic and cultural groups in the receiving country. The refugee crisis of 2015 has resulted in critical levels of opinion polarization on the question of whether to welcome migrants causing clashes in receiving countries. This scenario emphasizes the need to better understand the dynamics of mutual adaptation between locals and migrants and the conditions that favor successful integration. Agent-based simulations can help achieve this goal. In this work, we introduce our model MigrAgent and our preliminary results. The model synthesizes the dynamics of migration intake and postmigration adaptation. It explores the different acculturation outcomes that can emerge from the mutual adaptation of a migrant population and a local population depending on their degree of tolerance. With parameter sweeping, we detect how different acculturation strategies can coexist in a society and in different degrees among various subgroups. The results show higher polarization effects between a local population and a migrant population for fast intake conditions. When migrant intake is slow, transitory conditions between acculturation outcomes emerge for subgroups, for example, from assimilation to integration for liberal migrants and from marginalization to separation for conservative migrants. Relative group sizes due to speed of intake cause counterintuitive scenarios such as the separation of liberal locals. We qualitatively compare the processes of our model with the German portion sample of the survey “Causes and Consequences of Socio-Cultural Integration Processes Among New Immigrants in Europe,” finding preliminary confirmation of our assumptions and results.

Rocco PaolilloWander Jager

Keywords acculturationmigrationtolerancepolarizationagent-based simulation

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Best reply structure and equilibrium convergence in generic games

Abstract

Game theory is widely used to model interacting biological and social systems. In some situations, players may converge to an equilibrium, e.g., a Nash equilibrium, but in other situations their strategic dynamics oscillate endogenously. If the system is not designed to encourage convergence, which of these two behaviors can we expect a priori? To address this question, we follow an approach that is popular in theoretical ecology to study the stability of ecosystems: We generate payoff matrices at random, subject to constraints that may represent properties of real-world games. We show that best reply cycles, basic topological structures in games, predict nonconvergence of six well-known learning algorithms that are used in biology or have support from experiments with human players. Best reply cycles are dominant in complicated and competitive games, indicating that in this case equilibrium is typically an unrealistic assumption, and one must explicitly model the dynamics of learning.
Marco Pangallo1,2,*, Torsten Heinrich1,2,3 and J. Doyne Farmer1,2,4,5

https://advances.sciencemag.org/content/5/2/eaat1328?fbclid=IwAR2H7P660JrThTBPcmPq_R01ewkuXQSUeAt8T47TB5ijB9FxgJYLvFulEfU

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Machine learning and behavioral economics for personalized choice architecture

Emir Hrnjic and Nikodem Tomczak

Abstract: Behavioral economics changed the way we think about market participants and revolutionized policy-making by introducing the concept of choice architecture. However, even though effective on the level of a population, interventions from behavioral economics, nudges, are often characterized by weak generalisation as they struggle on the level of individuals. Recent developments in data science, artificial intelligence (AI) and machine learning (ML) have shown ability to alleviate some of the problems of weak generalisation by providing tools and methods that result in models with stronger predictive power. This paper aims to describe how ML and AI can work with behavioral economics to support and augment decision-making and inform policy decisions by designing personalized interventions, assuming that enough personalized traits and psychological variables can be sampled.

Fai clic per accedere a 1907.02100.pdf

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What is Complexity Science?

I think the next [21st] century will be the century of complexity” – Stephen Hawking

Complexity science, also called complex systems science, studies how a large collection of components – locally interacting with each other at small scales – can spontaneously self-organize to exhibit non-trivial global structures and behaviors at larger scales, often without external intervention, central authorities or leaders. The properties of the collection may not be understood or predicted from the full knowledge of its constituents alone. Such a collection is called a complex system and it requires new mathematical frameworks and scientific methodologies for its investigation.

Here are a few things you should know about complex systems,
result of a worldwide collaborative effort from leading experts, practitioners and students in the field.

https://complexityexplained.github.io/?fbclid=IwAR2kuU-NydZ7mxdzlNEvnG37HWnWI3zN5mjF20ZcgBPCXnjakARpGAyAbNw

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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)
https://halshs.archives-ouvertes.fr/halshs-02160907/document 

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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.

https://medium.com/this-is-hcd/gamification-a-new-form-of-human-centred-design-6a45065ce11b

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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)
https://www.economics.uci.edu/research/wp/1819/18-19-08.pdf (application/pdf)

Stergios Skaperdas (sskaperd@uci.edu) and Samarth Vaidya (samarth.vaidya@deakin.edu.au)
Additional contact information

No 181908, Working Papers from University of California-Irvine, Department of Economics

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THE INHERENT INSTABILITY OF DISORDERED SYSTEMS

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.

 

Abstract

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.

https://necsi.edu/the-inherent-instability-of-disordered-systems?fbclid=IwAR1SbanBKzLaVnMVIgB-sKJ366Rhip3ahl2XHUNfkuT22YtHlRsKUQGRDEQ




Time is real? I think not

luglio: 2019
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