agent-based simulation
1.4K views | +0 today
Follow
agent-based simulation
I'm an economist always reasoning about my profession
Curated by Pietro Terna
Your new post is loading...
Your new post is loading...
Scooped by Pietro Terna
Scoop.it!

Data-driven agent-based modeling as an approach in computational social science - Google Slides

Data-driven agent-based modeling as an approach in computational social science - Google Slides | agent-based simulation | Scoop.it
Data-driven Agent-based Modeling as an Approach in Computational Social Science Jan Lorenz Twitter: @jalorenz Nov 8, 2018, DIGITAL TRACES WORKSHOP, Methodenzentrum Uni Bremen...
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Computational Economics, Volume 52, Issue 3 - Springer

Computational Economics, Volume 52, Issue 3 - Springer | agent-based simulation | Scoop.it
Browse Volumes & Issues Volume 52, Issue 3, October 2018 Special Issue: Evolutionary Dynamics and Agent-Based Modeling in Economics Issue Editors: Herbert Dawid, Andreas Pyka … show all 2 hide ISSN: 0927-7099 (Print) 1572-9974 (Online) In this issue (11 articles) EditorialNotes Introduction: Special Issue on Evolutionary Dynamics and Agent-Based Modeling in Economics Herbert Dawid, Andreas Pyka Pages 707-710 Download PDF (306KB) View Article OriginalPaper Estimation of Sentiment Effects in Financial Markets: A Simulated Method of Moments Approach Zhenxi Chen, Thomas Lux Pages 711-744 OriginalPaper Modeling Firm and Market Dynamics: A Flexible Model Reproducing Existing Stylized Facts on Firm Growth Thomas Brenner, Matthias Duschl Pages 745-772 OriginalPaper The Role of Network Topology and the Spatial Distribution and Structure of Knowledge in Regional Innovation Policy: A Calibrated Agent-Based Model Study Ben Vermeulen, Andreas Pyka Pages 773-808 Download PDF (4050KB) View Article OriginalPaper Network Externalities and Compatibility Among Standards: A Replicator Dynamics and Simulation Analysis Torsten Heinrich Pages 809-837 Download PDF (1348KB) View Article OriginalPaper The Impact of Credit Rating on Innovation in a Two-Sector Evolutionary Model Pascal Aßmuth Pages 839-872 OriginalPaper The Limits to Credit Growth: Mitigation Policies and Macroprudential Regulations to Foster Macrofinancial Stability and Sustainable Debt Sander van der Hoog Pages 873-920 OriginalPaper Evolutionary Climate-Change Modelling: A Multi-Agent Climate-Economic Model Sylvie Geisendorf Pages 921-951 OriginalPaper Agent-Based Analysis of Industrial Dynamics and Paths of Environmental Policy: The Case of Non-renewable Energy Production in Germany Frank Beckenbach, Maria Daskalakis, David Hofmann Pages 953-994 OriginalPaper Endogenous Economic Growth, Climate Change and Societal Values: A Conceptual Model Michael W. M. Roos Pages 995-1028 Download PDF (1955KB) View Article OriginalPaper Assortative Matching with Inequality in Voluntary Contribution Games Stefano Duca, Dirk Helbing, Heinrich H. Nax Pages 1029-1043 Download PDF (545KB) View Article
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

[1809.08146] A Game of Tax Evasion: evidences from an agent-based model

Download: PDF Other formats (license) Current browse context: q-fin.GN < prev | next > new | recent | 1809 Change to browse by: References & Citations NASA ADS Quantitative Finance > General Finance A Game of Tax Evasion: evidences from an agent-based model L. S. Di Mauro, A. Pluchino, A. E. Biondo (Submitted on 21 Sep 2018) This paper presents a simple agent-based model of an economic system, populated by agents playing different games according to their different view about social cohesion and tax payment. After a first set of simulations, correctly replicating results of existing literature, a wider analysis is presented in order to study the effects of a dynamic-adaptation rule, in which citizens may possibly decide to modify their individual tax compliance according to individual criteria, such as, the strength of their ethical commitment, the satisfaction gained by consumption of the public good and the perceived opinion of neighbors. Results show the presence of thresholds levels in the composition of society - between taxpayers and evaders - which explain the extent of damages deriving from tax evasion. Comments: Subjects: General Finance (q-fin.GN); Physics and Society (physics.soc-ph) Cite as: arXiv:1809.08146 [q-fin.GN]   (or arXiv:1809.08146v1 [q-fin.GN] for this version) Submission history From: Alessandro Pluchino [view email] [v1] Fri, 21 Sep 2018 14:34:58 GMT (810kb,D) Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

The Atlas of Economic Complexity

The Atlas of Economic Complexity | agent-based simulation | Scoop.it
Visualize global trade data and economic growth opportunities for every country
Pietro Terna's insight:
Atlas Harvard version
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

On the role of collective sensing and evolution in group formation

On the role of collective sensing and evolution in group formation | agent-based simulation | Scoop.it
Collective sensing is an emergent phenomenon which enables individuals to estimate a hidden property of the environment through the observation of social interactions. Previous work on collective...
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Agents, Agents Everywhere

Agents, Agents Everywhere | agent-based simulation | Scoop.it
Rethinking Economics is an international network of students, academics and professionals building a better economics in society and the classroom.
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Price Competition with Particle Swarm Optimization: An Agent-Based Artificial Model - AgEcon Search

This study instructs an artificial price competition market to examine the impact of capacity constraints on the behavior of packers. Results show when there are cattle left for the lowest bidder after all other packers finishing their procurement, the capacity constraints make the price lower...
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Complexity Explorables | Complexity Explorables

Complexity Explorables | Complexity Explorables | agent-based simulation | Scoop.it
Interactive explorations of complex systems in biology, physics, mathematics, social sciences, ecology, epidemiology and ....
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Agent Based Modeling of Complex Adaptive Systems (Basic)

Agent Based Modeling of Complex Adaptive Systems (Basic) | agent-based simulation | Scoop.it
Our human society consists of many intertwined Large Scale Socio-Technical Systems (LSSTS), such as infrastructures, industrial networks, the financial systems etc. Environmental pressures created by these systems on Earth’s carrying capacity are leading to exhaustion of natural resources, loss of habitats and biodiversity, and are causing a resource and climate crisis. To avoid this sustainability crisis, we urgently need to transform our production and consumption patterns. Given that we, as inhabitants of this planet, are part of a complex and integrated global system, where and how should we begin this transformation? And how can we also ensure that our transformation efforts will lead to a sustainable world? LSSTS and the ecosystems that they are embedded in are known to be Complex Adaptive Systems (CAS). According to John Holland CAS are “…a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of a CAS tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it will have to to arise from competition and cooperation among the agents themselves. The overall behavior of the system is the result of a huge number of decisions made every moment” by many individual agents. Understanding Complex Adaptive Systems requires tools that themselves are complex to create and understand. Shalizi defines Agent Based Modeling as “An agent is a persistent thing which has some state we find worth representing, and which interacts with other agents, mutually modifying each other’s states. The components of an agent-based model are a collection of agents and their states, the rules governing the interactions of the agents and the environment within which they live.” This course will explore the theory of CAS and their main properties. It will also teach you how to work with Agent Based Models in order to model and understand CAS.
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Agent based model 3: Percolation of consumer products 2 - Decision Making in a Complex and Uncertain World - University of Groningen

Agent based model 3: Percolation of consumer products 2 - Decision Making in a Complex and Uncertain World - University of Groningen | agent-based simulation | Scoop.it
3.11 Unable to play video. Please enable JavaScript or consider upgrading your browser. View transcript 0:12Skip to 0 minutes and 12 secondsHere we have a very large grid where the interpolation process will be demonstrated. I will just run a few steps. And as you can see, the upper left corner and the lower left corner, the first agents adopt the new product. And they share the information about the quality of the product, as explained just before. So let's see how this continues. We see that the product spreads through society. We also see a lot of places where agents do not adopt the product because the quality is quite low. What is most fascinating is that we end with a very large, wide gap of agents not adopting. 1:06Skip to 1 minute and 6 secondsThis is a bit weird because you would expect that if the agents have distribution of preferences, that at least here would be some agents that would be interested in the product. So why don't they adopt? Well, because there's a borderline here of agents that think that the quality of the product is not sufficient. And remember, when the quality is too low to adopt, they will not further spread the information about the quality of the product. Hence, here we have a large zone of where the agents are not being informed about the existence of the product. This is a very interesting phenomenon that was being identified by the econophysicist. 2:00Skip to 2 minutes and 0 secondsBut obviously in our real social world, this is very rarely to happen, because we have very rich ways of communication. Agent based model 3: Percolation of consumer products 2 This video shows a larger grid to demonstrate the percolation process. © University of Groningen Share this video:
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

The crisis of economics and Agent Based Models | Economia e Politica

The crisis of economics and Agent Based Models | Economia e Politica | agent-based simulation | Scoop.it
Agent based models (ABM) is a methodology that can be applied to any model. It is valid for the DSGE as well as for “amateurs”: the Walras model is model of agents arranged on a star network, while the DSGE [e.g. Smets; Wouters, 2003] has identical agents, or non-interacting heterogeneous ones – that is, without networks. While the former do not show those aspects of complexity which are related to the real world, the present work is dedicated to the ones where the HIA generate complexity. “Agent based models” make it possible to overcome some of the many contradictions of the axiomatic model, whether it be reconciling reality and theory, or making the construction a dynamic one: innovation, for example, becomes the engine of the economy and not a stumbling block. Paradoxically, the axiomatic theory, instead of acknowledging the numerous cases of market failure, always prescribes further market expansion as a way to restore the mainstream economics. Although this theory admits that in reality the market fails to be efficient, nothing is done to review the regulatory requirements which give the market itself the function to act as a tool to reach that “optimality” of which we spoke. Because, according to the mainstream theory, if the Pareto principle is an efficiency that cannot be obtained in reality it is only because the markets aren’t widespread enough. An excellent example of confusion between dreams, reality and the wishes of numerous small make-believe Napoleons who are convinced they can fight the battle of Austerlitz again. Better not fall into the trap of discussing military strategies with them, because it would be like acknowledging that we really are in front of Bonaparte, as Robert Solow (1984) says. Economics is in crisis from at least two different perspectives. The first is the crisis of the GDP growth concept as a means of spreading economic well-being by means of increased employment. Job-less growth is a phenomenon now common to all economies since the mid-90s and this has led to talk about agrowth. Furthermore – as Pijpers notes, 2018: 3-4 – “Within complex systems there is a known phenomenon referred to by the term “self-organized criticality”. It appears that many systems have the property that, through the interactions of the elements within the system, they reach a critical state. As long as there is no external stimulus or disruption to this system, it appears to be in a stable equilibrium. However, a very small disturbance of the system in this critical state can cause very far-reaching changes. In this case a traditional approach, where the behaviour over the recent past is used as an indicator of future stability or robustness, is clearly not suitable. Some small stimuli might cause widespread disruption, and others none at all. An arbitrary external stimulus of a system that is in such a critical condition will not as a matter of course produce an instability or other “catastrophic” behavior. Modelling is a means of assessing in what ways, i.e. for which incentives or stimuli, an economy is vulnerable/fragile and which stimuli are harmless, i.e. leave the economy robust or sustainable.” Then, the economic crisis, which has been on-going since 2007 with GDP slumps, bankruptcies, deflationary phenomena and levels of unemployment not seen since the Great Depression questioned about the relevance of the current mainstream. And it seems as if the only way to tackle it, is with exceptional measures. This aspect is intertwined with the fact that the dominant macroeconomic model was unable to predict the crisis because it didn’t contemplate a priori any possibility of a long, deep crisis, like the one we are currently experiencing. This is why the crisis has also affected the paradigm, just like the Depression of 1929 did. That crisis was resolved with the New Deal and, at the same time, also with the Keynesian revolution. Perhaps it’s not true that the future enters us, to be transformed already within us, long before it actually happens, but if we continue with the same demographic and production trends, in 2030 we will need the resources of two Earths. So we must either prepare ourselves to colonize the Universe or to immediately start redistributing resources and thinking about sustainability. The crisis of mainstream economics is well documented by academic works and central bankers’ contributions. In my opinion, a fundamental feature of macroeconomic modelling resides in the ability to analyze evolutionary complex systems like the economic one. What characterizes a complex system is the notion of emergence, that is the spontaneous formation of self-organized structures at different layers of a hierarchical system configuration. Agent Based Modelling is a methodological instrument – that can be use- fully employed by both neoclassical or Keynesian economists, or whatever theoretical approach. [Reconciling DSGE and complex ABM seems like the calculation of Tycho Brahe who proposed a model that replaced the Ptolemaic one among all those astronomers who did not want to accept the Earth’s movement. From a cinematic perspective, the Tychonic model is identical to the Copernican one. The two models differ only by their chosen reference system: the Earth for Brahe, the Sun for Copernicus.] – which is appropriate to study complex dynamics as the result of the interaction of heterogeneous agents (where a degenerate case would be a “representative agent” model in which the degree of both heterogeneity and interaction is set to zero, that is a situation that reduces holism to reductionism in a hypothetical world without networks and coordination problems). Even when fluctuations of agents occur around equilibrium which we could calculate using the standard approach, the ABM analyses would not necessarily lead to the same conclusions. This is because the characteristics of the fluctuations would depend on higher moments of the joint distribution and often on the properties of the tails, or three kurtosis of the distribution. For the last couple of decades ABM have seriously taken to heart the concept of economy as an evolving complex system [Anderson et al., 1988]. Two keywords characterize this approach: Evolving, which means the system is adaptive through learning; and ABM complex, i.e. a methodology that allows to construct, based on simple (evolving) rules of behavior and interaction, models with heterogeneous interacting agents, where the resulting aggregate dynamics and empirical regularities not known a priori and not deducible from individual behavior. Agents’ behavioral rules are not fixed (this does not mean that it is not legitimate to build ABMs with fixed rules, for example, to understand what the dynamics of an economic system would be if agents behaved in an “optimal” way), but change adapting to variations of the economic environment in which they interact. The traditional approach which assumes optimizing agents with rational expectations has been and is a powerful tool for deriving optimal behavioral rules that are valid when economic agents have perfect knowledge of their objective function, and it is common knowledge that all agents optimize an objective function which is perfectly known. If agents are not able to optimize, or the common knowledge property is not satisfied, the rules derived with the traditional approach lose their optimality and become ad hoc rules. Research is still far from being complete, above all where empirical verification of aggregate models is concerned, but it is already more effective in explaining reality than what has been done so far and continues to be done by the DSGE models that dominate the economic scene. Although ABM certainly do not constitute a panacea for the crisis, it is indisputable that they provide suggestions of economic policy un- known in traditional models (network, domino effects, resilience and fragility, etc.). Freed from the straightjacket of equilibrium and representative agent hypothesis, that only works with a single good and a single market, we can finally dedicate time to investigate the potentiality of interactive agents and their emergent properties. The complex ABM approach can offer new answers to new and old unsolved questions, although it is still in a far too premature stage to offer definitive tools. This book shows that this new tool has already yielded interesting results and also that this approach does not say different things in simple situations where the comparison with the standard models is possible. It enables analysis of complex situations that are difficult to analyze with the models most in use today. We need a paradigm that knows how to conjugate economic, social and environmental aspects. A secular paradigm, free from that axiomaticism which is characteristic of the current economic mainstream. I don’t want to affirm that complex ABM models will be the starting point of future economic theory. But nearly. Tratto da Gallegati M., “Complex Agent Based Models”, Springer, June 2018
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Urbanisation and Complex Systems –

Urbanisation and Complex Systems – | agent-based simulation | Scoop.it
Colin Harrison, IBM Distinguished Engineer Emeritus (retired), formerly lead the development of technical strategy for IBM’s Smarter Cities initiative.The city is humanity’s greatest inventi…...
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Validating ABMs (SIMSOC discussion)

Validating ABMs (SIMSOC discussion) | agent-based simulation | Scoop.it
Photo by MeriçDağlı on Unsplash.On 12th September 2018, I asked the SIMSOC email list for advice on how to validate an empirical agent-based model in th...
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

An agent-based model for financial vulnerability

An agent-based model for financial vulnerability | agent-based simulation | Scoop.it
This study addresses a critical regulatory shortfall by developing a platform to extend stress testing from a microprudential approach to a dynamic, macroprudential approach. This paper describes the...
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

GIS and Agent-Based Modeling: MASON Update

GIS and Agent-Based Modeling: MASON Update | agent-based simulation | Scoop.it
This blog is a research site focused around my interests in Geographical Information Science (GIS) and Agent-Based Modeling (ABM).
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Kinetic Contagion Model Captures Collective Motion in Fearful Crowds

Kinetic Contagion Model Captures Collective Motion in Fearful Crowds | agent-based simulation | Scoop.it
By Lina Sorg
In social psychology, emotional contagion occurs when one individual bases his/her emotional response on the emotions and reactions of surrounding individuals. This phenomenon frequently arises in high-stress crowds — complex, dynamic systems comprised of many single particles acting...
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Why the social simulation community should tackle prediction

Why the social simulation community should tackle prediction | agent-based simulation | Scoop.it
By Gary Polhill Thread4 On 4 May 2002, Scott Moss (2002) reported in the Proceedings of the National Academy of Sciences of the United States of America that he had recently approached the e-mail discussion list of the International Institute of Forecasters to ask whether anyone had an example of...
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

AGENT‐BASED MACROECONOMICS AND DYNAMIC STOCHASTIC GENERAL EQUILIBRIUM MODELS: WHERE DO WE GO FROM HERE? - Dilaver - 2018 - Journal of Economic Surveys - Wiley Online Library

Agent‐based computational economics (ACE) has been used for tackling major research questions in macroeconomics for at least two decades. This growing field positions itself as an alternative to dynamic stochastic general equilibrium (DSGE) models. In this paper, we provide a much needed review and synthesis of this literature and recent attempts to incorporate insights from ACE into DSGE models. We first review the arguments raised against DSGE in the macroeconomic ACE (macro ACE) literature, and then review existing macro ACE models, their explanatory power and empirical performance. We then turn to the literature on behavioural New Keynesian models that attempts to synthesize these two approaches to macroeconomic modelling by incorporating insights of ACE into DSGE modelling. Finally, we provide a thorough description of the internally rational New Keynesian model, and discuss how this promising line of research can progress.
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Green Tea Press – Free books by Allen B. Downey

Green Tea Press – Free books by Allen B. Downey | agent-based simulation | Scoop.it
Welcome to Green Tea Press, publisher of Think Python, Think Bayes, and other books by Allen Downey. Read our Textbook Manifesto. Free Books! All of our books are available under free licenses that allow readers to copy and distribute the text; they are also free to modify it, which allows them to adapt the book to different needs, and to help develop new material. These books are available in a variety of electronic formats; some are also for sale in hard copy. Available now Think Python 2e: How To Think Like a Computer Scientist An introduction to programming using Python, one of the best programming languages for beginners.  The second edition uses Python 3. Think Python 1e: How To Think Like a Computer Scientist The first edition of Think Python, using Python 2. Think Perl 6: How to Think Like a Computer Scientist A new introduction to Perl 6 by Laurent Rosenfeld. Data Science in Python Think Stats, 2nd Edition An introduction to exploratory data analysis. Like the first edition, this book emphasizes simple computational tools for exploring real data. It includes several new topics, including regression, time series analysis and survival analysis. It presents basic use of NumPy, SciPy, pandas, and StatsModels.    This book revises and extends the first edition, Think Stats: Probability and Statistics for Programmers Think Bayes: Bayesian Statistics in Python An introduction to Bayesian statistics using simple Python programs instead of complicated math. Think DSP: Digital Signal Processing in Python An introduction to digital signal processing with applications to sound and image processing. Java Think Java: How To Think Like a Computer Scientist New edition, revised and updated by Chris Mayfield and Allen Downey, and published by O’Reilly Media. Think Data Structures: Algorithms and Information Retrieval in Java Build your own Web search engine—including a crawler, indexer, and search interface—while learning about data structures and algorithms in Java. New books in progress Modeling and Simulation in Python Models of discrete systems, like population growth, first-order systems, like epidemics and thermal systems, and second-order systems, like mechanical systems.  For people who have not programmed before. Think Complexity 2e: Exploring Complexity Science with Python An introduction to complexity science, which includes small world graphs, scale-free networks, cellular automata, fractals and pink noise, self-organized criticality, and agent-based models. Think OS: A Brief Introduction to Operating Systems An introduction to Operating Systems for programmers. Uses the C programming language. How to Think Like a Computer Scientist How to Think Like a Computer Scientist is an introductory programming book for people who have never programmed before, available for several programming languages: How To Think Like a Computer Scientist: C++ Version How To Think Like a (Functional) Programmer: OCaml Version Python for Software Design: How To Think Like a Computer Scientist How To Think Like a Computer Scientist: Learning with Python (this book has now been replaced by Think Python). Also from Green Tea Press The Little Book of Semaphores Learn about software synchronization by solving a series of puzzles. Physical Modeling in MATLAB Use MATLAB to predict and explain the behavior of physical systems.  Intended for people with no programming experience. More Free Computer Science Books Max Hailperin’s Operating Systems and Middleware: Supporting Controlled Interaction is now available under a Creative Commons license. About free books If you enjoy these books, please read about Five Easy Ways to Help Promote Free Books. If you are thinking about writing a free book, here are reasons you should and suggestions about how: Free Books: Why Not?.
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Agent cognition through micro-simulations: Adaptive and tunable intelligence with NetLogo LevelSpace. (arXiv:1807.10847v1 [cs.AI]) – Newsemia

Agent cognition through micro-simulations: Adaptive and tunable intelligence with NetLogo LevelSpace. (arXiv:1807.10847v1 [cs.AI]) – Newsemia | agent-based simulation | Scoop.it
Latest Medical News & Articles...
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Macroeconomic Policy in DSGE and Agent-Based Models Redux

Macroeconomic Policy in DSGE and Agent-Based Models Redux | agent-based simulation | Scoop.it
by Giorgio Fagiolo and Andrea Roventini...
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

Complexity Economics | Exploring Economics

Complexity Economics | Exploring Economics | agent-based simulation | Scoop.it
<h5>Author: Joeri Schasfoort | 18th September 2017<br />
Scientific review will follow soon</h5>

<h2>1. Core elements</h2>

<p style="text-align:justify">Complexity economics is the study of economic systems as complex systems.
more...
No comment yet.
Scooped by Pietro Terna
Scoop.it!

PyCX: a Python-based simulation code repository for complex systems education

PyCX: a Python-based simulation code repository for complex systems education | agent-based simulation | Scoop.it
We introduce PyCX, an online repository of simple, crude, easy-to-understand sample codes for various complex systems simulation, including iterative maps, cellular automata, dynamical networks and...
more...
No comment yet.