Bounded Rationality and Beyond
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News on the effects of bounded rationality in economics and business, relationships and politics
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23 bias cognitivi che ti stanno incasinando la vita

23 bias cognitivi che ti stanno incasinando la vita | Bounded Rationality and Beyond | Scoop.it

Conosci i bias cognitivi? Sono dei cortocircuiti del nostro cervello che possono portarci a prendere decisioni molto stupide. Se li conosci, li eviti.

Ti è mai capitato di avere quella fastidiosa sensazione di essere il peggior nemico di te stesso? Ti consideri una persona mediamente intelligente, sai distinguere ciò che è giusto da ciò che è sbagliato, eppure ti ritrovi, più spesso di quanto vorresti, a prendere decisioni stupide: sgarri alimentari che ti riempiono di sensi di colpa, acquisti impulsivi che si dimostrano inutili, impegni procrastinati che non fanno altro che generare stress. La nostra mente è senza dubbio una delle più raffinate creazioni della natura, eppure ogni tanto va in “tilt” e ci fa comportare come degli asini totali. Perché? E’ evitabile? Come possiamo prendere decisioni migliori, decisioni che ci avvicinino ai nostri traguardi, invece che allontanarcene?

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Nudge e elezioni. Intervista a Paolo Moderato –

Nudge e elezioni. Intervista a Paolo Moderato – | Bounded Rationality and Beyond | Scoop.it
Se in questi ultimi scampoli di campagna elettorale, in vista dell’appuntamento del 4 marzo, diventa sempre più faticoso riscontrare un approccio razionale – e, dunque, evidence based – alle varie proposte politiche in campo, un aiuto in questo senso può venire dalle scienze del comportamento. Scienze che non sono affatto nuove nel loro ruolo di partner delle politiche pubbliche, in ambito anglosassone: ce lo spiega Paolo Moderato, Ordinario di Psicologia Generale, presso l’Università IULM di Milano e presidente di IESCUM – Istituto Europeo per lo Studio del Comportamento Umano, think tank che da oltre un decennio si occupa della ricerca nel campo delle behavioral sciences.
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Rational Heuristics? Expectations and Behaviors in Evolving Economies with Heterogeneous Interacting Agents

Rational Heuristics? Expectations and Behaviors in Evolving Economies with Heterogeneous Interacting Agents | Bounded Rationality and Beyond | Scoop.it
Downloadable! We analyze the individual and macroeconomic impacts of heterogeneous expectations and action rules within an agent-based model populated by heterogeneous, interacting firms. Agents have to cope with a complex evolving economy characterized by deep uncertainty resulting from technical change, imperfect information and coordination hurdles. In these circumstances, we find that neither individual nor macroeconomic dynamics improve when agents replace myopic expectations with less naie learning rules. In fact, more sophisticated, e.g. recursive least squares (RLS) expectations produce less accurate individual forecasts and also considerably worsen the performance of the economy. Finally, we experiment with agents that adjust simply to technological shocks, and we show that individual and aggregate performances dramatically degrade. Our results suggest that fast and frugal robust heuristics are not a second-best option: rather they are "rational" in macroeconomic environments with heterogeneous, interacting agents and changing "fundamentals".
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Learning by omission | Santa Fe Institute

Learning by omission | Santa Fe Institute | Bounded Rationality and Beyond | Scoop.it

While scientists don’t fully understand how machine-learning algorithms have succeeded at “intelligent” tasks like image and speech recognition, they do know that in order to generalize, an algorithm has to remember the important information while forgetting the useless. This idea, often referred to as an “Information Bottleneck,” has generated a flurry of research since it was first proposed in 2000. Only very recently, however, has this idea been applied to the rapidly developing field of deep learning, i.e., machine learning that uses so-called artifcial neural networks. What would happen if neural networks were explicitly trained to discard useless information, and how to tell them to do so, is the subject of new research by SFI Postdoctoral Fellows Artemy Kolchinsky, Brendan Tracey, and Professor David Wolpert.

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How do your friends plan to vote? | Santa Fe Institute

How do your friends plan to vote? | Santa Fe Institute | Bounded Rationality and Beyond | Scoop.it

Most election polls take the political pulse of a state or nation by reaching out to citizens about their voting plans. SFI Professor Mirta Galesic says pollsters might also ask: how do your friends plan to vote? In a new paper published in Nature Human Behavior, Galesic and co-authors — including Wandi Bruine de Bruin (Leeds Business School), former SFI Omidyar Fellow Marion Dumas (London School of Economics), and researchers from University of Southern California Dornsife — compared the results of these two polling methods from data gathered during the most recent presidential elections in the United States and France. They tapped into existing polling efforts to ask questions about an individual’s friends alongside the traditional questions about themselves. In both countries, responses people gave about their friends led to more accurate predictions for the election outcomes than the information people gave about themselves.

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Artificial intelligence faces reproducibility crisis

The booming field of artificial intelligence (AI) is grappling with a replication crisis, much like the ones that have afflicted psychology, medicine, and other fields over the past decade. Just because algorithms are based on code doesn't mean experiments are easily replicated. Far from it. Unpublished codes and a sensitivity to training conditions have made it difficult for AI researchers to reproduce many key results. That is leading to a new conscientiousness about research methods and publication protocols. Last week, at a meeting of the Association for the Advancement of Artificial Intelligence in New Orleans, Louisiana, reproducibility was on the agenda, with some teams diagnosing the problem—and one laying out tools to mitigate it.

 

Artificial intelligence faces reproducibility crisis
Matthew Hutson

Science  16 Feb 2018:
Vol. 359, Issue 6377, pp. 725-726
DOI: 10.1126/science.359.6377.725


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BIOTS - Blockchain + Internet of Things: Welcome to the Future!

Presentation by Dirk Helbing

 

See Also: 

Why our future world will (have to) be organized in a more decentralized way https://www.youtube.com/watch?v=rxfwNTwzyCw Next Civilization: The Impact of the Digital Revolution https://www.youtube.com/watch?v=M6lLfeLOlbA 

 


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The Seneca Effect: The Seneca Effect: why decline is faster than growth

The Seneca Effect: The Seneca Effect: why decline is faster than growth | Bounded Rationality and Beyond | Scoop.it

Don't you stumble, sometimes, into something that seems to make a lot of sense, but you can't say exactly why? For a long time, I had in mind the idea that when things start going bad, they tend to go bad fast. We might call this tendency the "Seneca effect" or the "Seneca cliff," from Lucius Annaeus Seneca who wrote that "increases are of sluggish growth, but the way to ruin is rapid." Could it be that the Seneca cliff is what we are facing, right now? If that is the case, then we are in trouble. With oil production peaking or set to peak soon, it is hard to think that we are going to see a gentle downward slope of the economy. Rather, we may see a decline so fast that we can only call it "collapse." The symptoms are all there, but how to prove that it is what is really in store for us? It is not enough to quote a Roman philosopher who lived two thousand years ago. We need to understand what factors might lead us to fall much faster than we have been growing so far. For that, we need to make a model and see how the various elements of the economic system may interact with each other to generate collapse. I have been working on this idea for quite a while and now I think I can make such a model. This is what the rest of this post will be about. We'll see that a Seneca cliff may indeed be part of our future if we keep acting as we have been acting so far (and as we probably will). But let's go into the details.

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Selective Maintenance of Value Information Helps Resolve the Exploration/Exploitation Dilemma

Laboratory studies of value-based decision-making often involve choosing among a few discrete actions. Yet in natural environments, we encounter a multitude of options whose values may be unknown or poorly estimated. Given that our cognitive capacity is bounded, in complex environments, it becomes hard to solve the challenge of whether to exploit an action with known value or search for even better alternatives. In reinforcement learning, the intractable exploration/exploitation tradeoff is typically handled by controlling the temperature parameter of the softmax stochastic exploration policy or by encouraging the selection of uncertain options. We describe how selectively maintaining high-value actions in a manner that reduces their information content helps to resolve the exploration/exploitation dilemma during a reinforcement-based timing task. By definition of the softmax policy, the information content (i.e., Shannon's entropy) of the value representation controls the shift from exploration to exploitation. When subjective values for different response times are similar, the entropy is high, inducing exploration. Under selective maintenance, entropy declines as the agent preferentially maps the most valuable parts of the environment and forgets the rest, facilitating exploitation. We demonstrate in silico that this memory-constrained algorithm performs as well as cognitively demanding uncertainty-driven exploration, even though the latter yields a more accurate representation of the contingency. We found that human behavior was best characterized by a selective maintenance model. Information dynamics consistent with selective maintenance were most pronounced in better-performing subjects, in those with higher non-verbal intelligence, and in learnable vs. unlearnable contingencies. Entropy of value traces shaped human exploration behavior (response time swings), whereas uncertainty-driven exploration was not supported by Bayesian model comparison. In summary, when the action space is large, strategic maintenance of value information reduces cognitive load and facilitates the resolution of the exploration/exploitation dilemma.


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Gil Kalai’s Argument Against Quantum Computers

Gil Kalai’s Argument Against Quantum Computers | Bounded Rationality and Beyond | Scoop.it

Sixteen years ago, on a cold February day at Yale University, a poster caught Gil Kalai’s eye. It advertised a series of lectures by Michel Devoret, a well-known expert on experimental efforts in quantum computing. The talks promised to explore the question “Quantum Computer: Miracle or Mirage?” Kalai expected a vigorous discussion of the pros and cons of quantum computing. Instead, he recalled, “the skeptical direction was a little bit neglected.” He set out to explore that skeptical view himself. Today, Kalai, a mathematician at Hebrew University in Jerusalem, is one of the most prominent of a loose group of mathematicians, physicists and computer scientists arguing that quantum computing, for all its theoretical promise, is something of a mirage. Some argue that there exist good theoretical reasons why the innards of a quantum computer — the “qubits” — will never be able to consistently perform the complex choreography asked of them. Others say that the machines will never work in practice, or that if they are built, their advantages won’t be great enough to make up for the expense.

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An interview based study of pioneering experiences in teaching and learning Complex Systems in Higher Education

Due to the interdisciplinary nature of complex systems as a field, students studying complex systems at University level have diverse disciplinary backgrounds. This brings challenges (e.g. wide range of computer programming skills) but also opportunities (e.g. facilitating interdisciplinary interactions and projects) for the classroom. However, there is little published regarding how these challenges and opportunities are handled in teaching and learning Complex Systems as an explicit subject in higher education, and how this differs in comparison to other subject areas. We seek to explore these particular challenges and opportunities via an interview-based study of pioneering teachers and learners (conducted amongst the authors) regarding their experiences. We compare and contrast those experiences, and analyse them with respect to the educational literature. Our discussions explored: approaches to curriculum design, how theories/models/frameworks of teaching and learning informed decisions and experience, how diversity in student backgrounds was addressed, and assessment task design. We found a striking level of commonality in the issues expressed as well as the strategies to handle them, for example a significant focus on problem-based learning, and the use of major student-led creative projects for both achieving and assessing learning outcomes.

 

J.T. Lizier, M.S. Harré, M. Mitchell, S. DeDeo, C. Finn, K. Lindgren, A.L. Lizier, H. Sayama

"An interview based study of pioneering experiences in teaching and learning Complex Systems in Higher Education"

arXiv:1802.02707, 2018


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How Coca-Cola, Netflix, and Amazon Learn from Failure

How Coca-Cola, Netflix, and Amazon Learn from Failure | Bounded Rationality and Beyond | Scoop.it

Encourage your team to embrace mistakes.

Why, all of a sudden, are so many successful business leaders urging their companies and colleagues to make more mistakes and embrace more failures? In May, right after he became CEO of Coca-Cola Co., James Quincey called upon rank-and-file managers to get beyond the fear of failure that had dogged the company since the “New Coke” fiasco of so many years ago. “If we’re not making mistakes,” he insisted, “we’re not trying hard enough.” In June, even as his company was enjoying unparalleled success with its subscribers, Netflix CEO Reed Hastings worried that his fabulously valuable streaming service had too many hit shows and was canceling too few new shows. “Our hit ratio is too high right now,” he told a technology conference. “We have to take more risk…to try more crazy things…we should have a higher cancel rate overall.”

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Controllability and Observability of Complex Systems - Yang-Yu Liu

OVERVIEW: The ultimate proof of our understanding of complex systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered systems towards a desired state, a framework to control complex systems is lacking. In this talk I will show that many dynamic properties of complex systems can studied be quantitatively, via a combination of tools from control theory, network science and statistical physics. In particular, I will focus on two dual concepts, i.e. controllability and observability, of general complex systems. Controllability concerns our ability to drive the system from any initial state to any final state within finite time, while observability concerns the possibility of deducing the system's internal state from observing its input-output behavior. I will show that by exploring the underlying network structure of complex systems one can determine the driver (or sensor) nodes that with time-dependent inputs (or measurements) will enable us to fully control (or observe) the whole system.Summer School in cognitive Science: Web Science and the Mind Institut des sciences cognitives, UQAM, Montréal, Canada http://www.summer14.isc.uqam.ca

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Il Nudge, l’economia comportamentale e le elezioni. Conversazione con Paolo Moderato (seconda parte) –

Il Nudge, l’economia comportamentale e le elezioni. Conversazione con Paolo Moderato (seconda parte) – | Bounded Rationality and Beyond | Scoop.it
La prima parte della conversazione con Paolo Moderato, Ordinario di Psicologia Generale presso l’Università IULM di Milano e presidente di IESCUM – Istituto Europeo per lo Studio del Comportamento Umano, puoi trovarla qui. In Italia c’è interesse per un approccio evidence based alle politiche pubbliche? «Nel nostro paese – spiega Moderato – l’interesse verso queste scienze è alto, tanto che nel corso degli anni si sono susseguite micro-iniziative sui territori: basti pensare alla nascente Nudge Unit voluta da Nicola Zingaretti nel Lazio, o alla partecipazione del nostro think tank – che è membro, per l’Italia, del TEN, il network europeo dei Nudge Team – con una iniziativa improntata al nudging durante la Festa dell’Unità romana, o anche alla nomina, da parte del Governo Renzi, di un esperto sul tema, il professor Matteo Motterlini. Tuttavia ancora manca un piano generale, una visione ampia, che porti ad iniziative come quella voluta dall’amministrazione di Barack Obama: un decreto che ha orientato l’intera macchina federale alla behavioral economics».
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EconPapers: Agent-Based Macroeconomics and Classical Political Economy: Some Italian Roots

Abstract: In this work, we discuss how the rich academic milieu left by different Italian political economy traditions after WWII paved the way to the development of a new generation of macroeconomic agent-based models. The K+S (Dosi et al., 2010, 2016a), CATS (Delli Gatti et al., 2005, 2011) and EURACE (Cincotti et al., 2010; Teglio et al., 2012) families of agent- based models are at the frontier of an alternative macroeconomic research paradigm which considers the economy as a complex evolving system. The three families of models are presented in details and their empirical performance and policy exercises discussed.

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Focus: Model Suggests Link between Intelligence and Entropy

Focus: Model Suggests Link between Intelligence and Entropy | Bounded Rationality and Beyond | Scoop.it

Dynamical systems that maximize their future possibilities behave in surprisingly “intelligent” ways.

The second law of thermodynamics—the one that says entropy can only increase—dictates that a complex system always evolves toward greater disorderliness in the way internal components arrange themselves. In Physical Review Letters, two researchers explore a mathematical extension of this principle that focuses not on the arrangements that the system can reach now, but on those that will become accessible in the future. They argue that simple mechanical systems that are postulated to follow this rule show features of “intelligence,” hinting at a connection between this most-human attribute and fundamental physical laws.

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The Behind-the-Scenes Impact of Emotional Intelligence

The Behind-the-Scenes Impact of Emotional Intelligence | Bounded Rationality and Beyond | Scoop.it

At this point, many people realize the general value of having leaders who are high achievers, have a positive outlook, are self-aware, or otherwise display some degree of emotional intelligence ability. Indeed, some 2,000 organizations worldwide measure emotional intelligence in current or prospective employees. But now there’s evidence that developing several of the so-called “soft” skills of emotional intelligence can not only help leaders manage any team but also encourage team members—as many as 70%, in fact—to stay five years or longer. These EI-enhanced leaders are much more effective because they can change their style to fit their teams, with the best employing up to four separate leadership styles well. “Effective leaders have multiple styles in their toolkits, equipping them to respond flexibly to changing demands,” says Paula Kerr, senior manager at the Korn Ferry Institute. Kerr, the author of a new Korn Ferry report, “The power of EI: The ‘soft’ skills the sharpest leaders use,” delves into how emotional intelligence skills such as self-awareness, empathy, and adaptability have quantifiable impacts on a leader’s performance. While most people recognize that these skills are no longer just “nice-to-have” attributes, Kerr says that fewer people understand the impact those emotional intelligence skills have on a leader’s style and employee loyalty.

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Latest Complexity Digest Posts

Latest Complexity Digest Posts | Bounded Rationality and Beyond | Scoop.it

Networking the complexity community since 1999

Contents from of the 02/26/2018 edition: Artificial intelligence faces reproducibility crisis Embracing Complexity An Interview with Jean Boulton A Two Teraflop Swarm Product diffusion through on-demand information-seeking behaviour Complexity72h Mixing and diffusion in a two-type population Rank dynamics of word usage at multiple scales Link transmission centrality in large-scale social networks

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Blog on Nudge and Traffic

Blog on Nudge and Traffic | Bounded Rationality and Beyond | Scoop.it
The curve at Lake Shore Drive and Oak Street in Chicago is a favorite nudge. The tight turn makes it one of the city’s most dangerous curves. To try and limit wrecks, in September 2006 the city painted a series of white lines perpendicular to traveling cars. The lines get progressively narrower as drivers approach the sharpest point of the curve, giving them the illusion of speeding up, and nudging them to tap their brakes.

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Optogenetics – Innovation in Biotechnology and Neuroscience

How the light and genetics may treat brain disorders in the future Imagine being able to treat neurodegenerative diseases and mental disorders such as Alzheimer’s disease, Parkinson’s, epilepsy, PTSD, depression, and anxiety with non-invasive light-based therapy. This is the quest of pioneering scientists and researchers in optogenetics, an emerging field in biotechnology that uses light to control cells in living tissues such as neurons, in order to study brain function. British Nobel laureate Francis Crick of The Salk Institute for Biological Studies in La Jolla, California put forth the concept of the ability to turn the firing of “one or more types of neuron on and off in the alert animal in a rapid manner” by using light as “the ideal signal” in his paper “The impact of molecular biology on neuroscience” published in Philosophical Transactions of the Royal Society B in 1999. Crick noted that his concept might be somewhat “far-fetched.” Yet as improbable as it would seem to the brightest minds in science before the turn of the century, this idea was proven in a little over half a decade.
https://www.linkedin.com/pulse/optogenetics-innovation-biotechnology-neuroscience-cami-rosso/?trackingId=qJ5ZnU0%2FWOAx5RXZAB3%2Fng%3D%3D
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COMPLEXITY, PROBLEM SOLVING, AND SUSTAINABLE SOCIETIES, by Joseph A. Tainter, 1996

OVERVIEW Historical knowledge is essential to practical applications of ecological economics. Systems of problem solving develop greater complexity and higher costs over long periods. In time such systems either require increasing energy subsidies or they collapse. Diminishing returns to complexity in problem solving limited the abilities of earlier societies to respond sustainably to challenges, and will shape contemporary responses to global change. To confront this dilemma we must understand both the role of energy in sustaining problem solving, and our historical position in systems of increasing complexity.
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AI’s Deep Problem

AI’s Deep Problem | Bounded Rationality and Beyond | Scoop.it

Artificial intelligence is modeled to some extent on the human brain; and there’s a deep problem with this approach. Machine learning is a subset of artificial intelligence (AI) where computer programs automatically learn from data without explicit programming. Inspired in part by the human biology, deep learning is a machine learning method that deploys layers of artificial neurons, called nodes, in an artificial brain called a neural network. Neuroscientists and psychologists have yet to fully understand how the human brain works. Similarly, there’s a big problem with deep learning; scientists do not really know exactly how deep learning reaches its decisions. In both cases, complexity is at the root of the lack of transparency. The human brain is complex; researchers estimate an adult male human brain to have 86 billion neurons on average [1]. Human neuroanatomy textbooks commonly gauge the number to be closer to 100 billion neurons. Similar to the human brain, deep learning consists of densely interconnected processing neurons, or nodes, arranged in multiple layers. Deep learning does not require explicit programming because it is designed to learn from vast amounts of input data. For example, Google’s deep learning program learned to recognize images of cats after being fed 10 million YouTube video thumbnails without hard coding or labeling the images [2].

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How disinformation spreads in a network

How disinformation spreads in a network | Bounded Rationality and Beyond | Scoop.it

Disinformation is kind of a problem these days, yeah? Fatih Erikli uses a simulation that works like a disaster spread model applied to social networks to give an idea of how disinformation spreads. I tried to visualize how a disinformation becomes a post-truth by the people who subscribed in a network. We can think this network as a social media such as Facebook or Twitter. The nodes (points) in the map represent individuals and the edges (lines) shows the relationships between them in the community. The disinformation will be forwarded to their audience by the unconscious internet (community) members. Set the “consciousness” parameter and select a node to run.

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An interview based study of pioneering experiences in teaching and learning Complex Systems in Higher Education –

An interview based study of pioneering experiences in teaching and learning Complex Systems in Higher Education – | Bounded Rationality and Beyond | Scoop.it
Due to the interdisciplinary nature of complex systems as a field, students studying complex systems at University level have diverse disciplinary backgrounds. This brings challenges (e.g. wide range of computer programming skills) but also opportunities (e.g. facilitating interdisciplinary interactions and projects) for the classroom. However, there is little published regarding how these challenges and opportunities…
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Talking Complex Systems Economics to people who understand complex systems | Prof Steve Keen on

Talking Complex Systems Economics to people who understand complex systems | Prof Steve Keen on | Bounded Rationality and Beyond | Scoop.it

Official Post from Prof Steve Keen: This talk was a pleasure to give, because for once I was talking to an audience which completely understands Complex Systems (unlike the vast majority of economists). In the case of "EASY"--the "Evolutionary and Adaptive Systems Research Group"(http://www.sussex.ac.uk/easy/)--they apply this methodo

This talk was a pleasure to give, because for once I was talking to an audience which completely understands Complex Systems (unlike the vast majority of economists). In the case of "EASY"--the "Evolutionary and Adaptive Systems Research Group"(http://www.sussex.ac.uk/easy/)--they apply this methodology to analysing the brain and consciousness, rather than economics. In this talk I: Outline Minsky (downloadable from https://sourceforge.net/projects/minsky/), the system dynamics platform I designed for economics to enable banks, debt and money to be easily incorporated into dynamic models of the economy, explain why mainstream economists believe that you don't have to include banks, debt and money in macroeconomic models and why they are profoundly wrong, discuss the attempt by some Neoclassical economists to get back to that Olde Religion now that the global economy is reviving somewhat, ten years after the Global Financial Crisis, and conclude by showing that macroeconomics does not have to be derived from microeconomics (which is impossible in the first place, because of emergent properties in complex evolutionary systems, which the economy manifestly is), but can instead be derived directly from macroeconomic definitions in a Complex Systems manner.

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