Bounded Rationality and Beyond
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Jobs in the UK Government for behavioural economists and psychologists

Jobs in the UK Government for behavioural economists and psychologists | Bounded Rationality and Beyond | Scoop.it
We received a letter from the Financial Services Authority in the UK. Sounds like some great job opportunities for readers of this website:

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Bounded Rationality and Beyond
News on the effects of bounded rationality in economics and business, relationships and politics
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Adaptive Capacity as Emergent Capacity

Center for Collective Dynamics of Complex Systems (CoCo) Seminar Series September 20, 2017 Pamela Mischen (Public Administration, Binghamton University) "Adaptive…
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Empatia e Neuroeconomia (prof. Guido Baggio)

Come si comporta il cervello nelle decisioni di tipo economico? La neuroeconomia, recente e affascinante frutto del dialogo tra economia, psicologia e neuroscienze, ha dimostrato che accanto al lato razionale hanno un ruolo fondamentale le emozioni nei processi decisionali e comportamentali di carattere economico. Il presupposto da cui parte l'analisi neuroeconomica è che, a differenza di quanto affermato dall'economia classica dell’homo oeconomicus, l'uomo non è un animale razionale, bensì agisce sotto l'impulso di processi neuronali automatici e molto spesso inconsci, quindi indipendenti dalla propria volontà. Ciò fa sì che il comportamento economico umano sia frutto di un conflitto neuronale tra razionalità ed emotività, automatismo e consapevolezza. A partire da queste premesse, l'obiettivo della neuroeconomia è quello di offrire un modello sufficientemente unitario della condotta umana che riesca a rendere conto dell’origine biologico-sociale tanto dei comportamenti economici centrati sull’interesse individuale quanto dei comportamenti economici cosiddetti altruisti, cioè capaci di tenere in considerazione l’appartenenza degli individui ad una comunità sociale.

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Here's what 180 years of prices in Sweden tell us about globalization

Here's what 180 years of prices in Sweden tell us about globalization | Bounded Rationality and Beyond | Scoop.it

New research looking at prices in Sweden, suggests intranational price convergence may be a 'precursor to globalization'.When you think globalisation began depends on how you define it. Ancient civilizations and medieval cities of course exchanged tools, plants, diseases, and ideas. But most recent discussions of the start of globalisation define it as integration of a market across regions or countries, as measured by the convergence of commodity prices. For example, The Economist (2013) argues there was a world silver market after about 1500. O’Rourke and Williamson (2002) argue that commodity price convergence is indeed the best way to measure the integration of markets that defines globalisation. They note the international trade in luxury goods – such as spices, silk, furs, and gold – from 1500 to 1800. But they suggest that falling transport costs led to a globalisation big bang in the 1820s marked by the integration of markets for necessities like grains and textiles. For the first time, this international integration began to affect how capital and labour were used within domestic economies, and thus also began to be the subject of political debates.

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What Economics Models Really Say - Evonomics

Why is there such an enormous gulf between what economists know and what they say in public?

The blurb on the jacket of Economics Rules says, “In this sharp, masterful book, Dani Rodrik, a leading critic from within, takes a close look at economics to examine when it falls short and when it works, to give a surprisingly upbeat account of the discipline.” I heartily agree with nearly all of this, with the exception of the “upbeat” part. As I will explain toward the end of this review, my view of economics, and, especially, of the role that economists play in public policy, is much more critical. A central theme in the book is the role of mathematical models in economics. Formal models in economics and other social sciences are often disparaged. According to the critics (who include some economists, many other social scientists, and the overwhelming majority of historians), models oversimplify complex reality, employ unrealistic assumptions, and deny “agency” to human beings.

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Social Preferences and Political Attitudes: An Online Experiment on a Large Heterogeneous Sample

Abstract This paper investigates – in a large heterogeneous sample – the relationship between social preferences on the one hand, and socioeconomic factors and political preferences on the other hand. Socioeconomic factors correlate with social preferences, and social preferences robustly shape political attitudes and voting behavior in a particular way: Selfish subjects are the extremists on one side of the political spectrum – they are more likely to vote for a rightwing party, they are less inclined to favor redistribution and they are more likely to self-assess themselves as right-wing than all the other types. Inequality-averse subjects, altruists and maxi-min sit at the opposite end of the political spectrum, while all the other types behave less systematically and in a less extreme fashion. Overall, our evidence indicates that elicited social preferences are externally valid as a predictor for political attitudes, and that social preferences are fairly stable across contexts and over longer periods of time.
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It Takes a Theory to Beat a Theory: The Adaptive Markets Hypothesis - Evonomics

We need a new narrative for how markets work. We now have enough pieces of the puzzle to start putting it all together.

After 2008, the wisdom of financial advisers and academics alike seemed naive and inadequate. So many millions of people had faithfully invested in the efficient, rational market: what happened to it? And nowhere did the financial crisis wound one’s professional pride more deeply than within academia. The crisis hardened a split among professional economists. On one side of the divide were the free market economists, who believe that we are all economically rational adults, governed by the law of supply and demand. On the other side were the behavioral economists, who believe that we are all irrational animals, driven by fear and greed like so many other species of mammals. Some debates are merely academic. This one isn’t. If you believe that people are rational and markets are efficient, this will largely determine your views on gun control (unnecessary), consumer protection laws (caveat emptor), welfare programs (too many unintended consequences), derivatives regulation (let a thousand flowers bloom), whether you should invest in passive index funds or hyperactive hedge funds (index funds only), the causes of financial crises (too much government intervention in housing and mortgage markets), and how the government should or shouldn’t respond to them (the primary financial role for government should be producing and verifying information so that it can be incorporated into market prices).

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The Most Intolerant Wins: The Dictatorship of the Small Minority

The Most Intolerant Wins: The Dictatorship of the Small Minority | Bounded Rationality and Beyond | Scoop.it
How Europe will eat Halal — Why you don’t have to smoke in the smoking section — Your food choices on the fall of the Saudi king –How to prevent a friend from working too hard –Omar Sharif ‘s…
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Big Picture of Big Data - Top 10 Big Data Trends in 2017 - DataFlair

Big Picture of Big Data - Top 10 Big Data Trends in 2017 - DataFlair | Bounded Rationality and Beyond | Scoop.it
Big data trends for 2017-top trends of big data analytics to understand big data predictions in 2017 for data scientists, data analysts and data engineers.
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A role for goal-oriented autonomous agents in modeling people-environment interactions in forest recreation

Abstract Increased demand by the public for diverse and quality recreation opportunities has placed considerable pressure on the natural resource and its management. This problem is compounded by a general lack of understanding of interactions between people and forest recreation environments that result in wide variations in perceptions, expectations, and patterns of choice and use. Emerging technologies, such as distributed artificial intelligence, provide a mechanism to integrate advances in recreation research with a Geographic Information Systems (GIS) based environment. Distributed artificial intelligence provides the foundation for a modeling system to simulate the interactions between recreators and their environment. Despite the work done by many researchers in the development of object-oriented modeling and simulation languages, GIS, nonhuman agent design and simulations, no single system has been constructed to handle the complexity of goal-oriented autonomous human agents seeking recreational opportunities in natural environments. This paper describes a theoretical framework and a model for simulating hiker behavior in a natural environment using intelligent agents, discrete event simulation (DEVS) and GIS data. The results of hiker interactions are summarized to provide feedback on the implications for alternative recreation management planning.
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WHAT DO WE KNOW THROUGH IMPROVISATION?

In this paper, which is exploratory in character, I address the question of whether, how, and what we know through artistic improvisation. Has artistic improvisation a specific cognitive supply? Can this alleged cognitive supply contribute to the aesthetic merit of the performance? And how? In order to answer these questions I will first explain which is exactly the problem we face. Secondly, I will try to give some suggestions to solve this problem.
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CONSTRAINT-DIRECTED IMPROVISATION FOR EVERYDAY ACTIVITIES By JOHN ERIC ANDERSON

ABSTRACT Existing approaches to planning in Artificial Intelligence (such as Universal and Classical Planning) are designed for very specific types of activities, and are largely inapplicable to areas outside their narrow ranges. In particular, everyday activities that are simple for humans, such as making a meal or getting from place to place, require long-term goal-directed and timely responses that are far beyond the bounds of these traditional approaches. This dissertation examines the nature of the everyday activities and develops a computational architecture for an agent able to participate in such activities. An analysis of everyday activities shows them to be difficult tasks made artificially simple through extensive activity-specific knowledge possessed by the agent performing them. I argue that existing approaches are unsuitable to everyday activity because they rely too heavily on compiled knowledge and fail to adequately apply the background knowledge from which these compilations were originally made. To address everyday activities, I present a theory of improvisation, a new approach that views the problem as satisficing intelligent control: providing resource-bounded responses to the environment in light of the agent’s previous experience and its current and future intentions for activity. This process is based on the use of both heavily compiled routines the agent is accustomed to following, and an extensive collection of background knowledge used to apply those routines flexibly. The agent can rely on its routines in normative situations or when time is too scarce to spend examining the reasons behind its routines, and can conversely rely more heavily on background knowledge as situations become less normative. This allows the agent to take advantage of regularities in its environment and respond flexibly in less familiar situations. I then present an architecture embodying the improvisational approach based on the use of constraint-directed reasoning. This methodology provides a flexible control mechanism that allows the agent to respond as dynamically as necessary for the circumstances in which it finds itself. Implemented examples of improvised behaviour are also shown, using a simulation tool developed in conjunction with this research. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.46.7382&rep=rep1&type=pdf
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Molecules react to their environment

Molecules react to their environment | Bounded Rationality and Beyond | Scoop.it

Does adaptation lead to a molecular Darwinism? Le Chatelier’s principle is neither hard to state nor to understand. But it’s kind of hard to find the right words. It’s typically expressed as something like: ‘If a dynamic equilibrium is disturbed by changing the conditions, the position of equilibrium moves to counteract the change.’ But there is a clear implication of intentionality here: it’s as though the system is determined to keep its balance. Sometimes, Le Chatelier’s principle is more or less equated with homeostasis in physiology – the maintenance of a steady state in a changing environment. Some homeostasis, such as pH regulation, does indeed involve the kind of shift in chemical equilibria described by Le Chatelier’s principle. The confusing thing is that biological homeostasis is also a survival mechanism and therefore connected to Darwinian adaptation. We have evolved sweat glands, yet the regulation of body temperature by sweating can be explained by purely physical laws. What this really means is that ‘adaptation to the environment’ has more than one meaning. It can refer to the gradual accommodation to a niche explained by natural selection in self-replicating systems, or to the instantaneous response to fluctuating environmental conditions due to physicochemical principles. These two processes interact, but one might have thought we could keep them distinct. After seeing a paper by Jordan Horowitz and Jeremy England of the Massachusetts Institute of Technology, US, I’m not so sure.

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Miklos Szilagyi's curator insight, August 14, 3:08 AM
Highly sophisticated piece (on the base (and extrapolating from) of a specified chemical experiment) about 2 things: (1) whether evolution is explaining evolution or some more basic physical/chemical/biological processes are also there to act and (for me at least) (2) whether equilibrium states are so general and universal we think they are... About point (2)... just thinking about it some days whether human race is not being left behind by the technological environment (in work and in privat life) it created itself on the first place... It seems to me that we are just living the moment of finally and irreversibly leaving our possibility to ever find a new balance in our life... not because we are individually not capable of finding it but because our constitution's incapability...
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Digital voting could revolutionise democracy, but it might come at a cost

Digital voting could revolutionise democracy, but it might come at a cost | Bounded Rationality and Beyond | Scoop.it

Digital technology could boost voter participation, but it comes with risks.  According to an unpublished “kitchen table survey,” conducted before last November’s presidential election in the United States, approximately 95% of the predominantly Hispanic members of one of America’s largest domestic unions preferred the Democratic candidate Hillary Clinton to her Republican opponent Donald Trump. Yet less than 3% of that union’s members actually planned to vote. The reason came down to economics.

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When our brain decides for us… And without our permission

When our brain decides for us… And without our permission | Bounded Rationality and Beyond | Scoop.it
(Originally published in Slovak at mindworx.net) Most of us probably believe that we are in control of our own decisions. We have our opinions, beliefs and principles, we know what we like and dislike and we always decide in accordance with our preferences. However, this is not entirely true. Our brain reacts to all kinds…
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An anatomy of murder – The Economist

An anatomy of murder – The Economist | Bounded Rationality and Beyond | Scoop.it
In the “fake news” era, few things are more important to a 174-year-old newspaper than hard facts. At The Economist this is perhaps best demonstrated in the kind of painstaking work done by our data…
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The emergence of altruism as a social norm

The emergence of altruism as a social norm | Bounded Rationality and Beyond | Scoop.it

Expectations, exerting influence through social norms, are a very strong candidate to explain how complex societies function. In the Dictator game (DG), people expect generous behavior from others even if they cannot enforce any sharing of the pie. Here we assume that people donate following their expectations, and that they update their expectations after playing a DG by reinforcement learning to construct a model that explains the main experimental results in the DG. Full agreement with the experimental results is reached when some degree of mismatch between expectations and donations is added into the model. These results are robust against the presence of envious agents, but affected if we introduce selfish agents that do not update their expectations. Our results point to social norms being on the basis of the generous behavior observed in the DG and also to the wide applicability of reinforcement learning to explain many strategic interactions.

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NeuroEconomia: Come il Cervello prende le Decisioni - Video Corso

NeuroEconomia: Come il Cervello prende le Decisioni - Video Corso | Bounded Rationality and Beyond | Scoop.it

Economia, Psicologia e Neuroscienze stanno oggi convergendo in una nuova disciplina chiamata Neuroeconomia, con l'obiettivo di fornire una unica teoria generale dei processi decisionali umani. La Neuroeconomia offre a economisti, psicologi e scienziati sociali una più profonda comprensione di come l'uomo prendere le decisioni, partendo dal presupposto che, a differenza di quanto affermato dall’economia tradizionale, l’uomo non è un animale razionale, ma agisce sotto l’impulso di processi neuronali automatici e molto spesso inconsci, quindi indipendenti dalla propria volontà. Il cervello é programmato per prendere rischi o per evitarli ? Come è valutata dal cervello una "decisione giusta" ? E possibile prevedere le intenzioni di acquisto di un consumatore ? Possiamo modulare il comportamento del cervello riguardo gli aspetti economici ? Per rispondere a queste domande e conoscere meglio le basi della Neuroeconomia a partire dal prossimo 23 Giugno la prestigiosa Higher School of Economics di Mosca, presenta un nuovo interessante video corso di 9 settimane totalmente gratuito, intitolato Introduction to Neuroeconomics: how the brain makes decisions che tratterà molti argomenti partendo dai fondamenti della neuroeconomia e neuroanatomia del cervello trattati in quattro moduli successivi, guarda la video-presentazione del corso :

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LE DISTORSIONI COGNITIVE. L'essere umano è davvero razionale?

Prima di parlare di bias cognitivi, è bene introdurre il concetto di euristiche: escamotage mentali che portano a conclusioni veloci con il minimo sforzo cognitivo, o meglio scorciatoie comode e rapide estrapolate dalla realtà. (2002 Kahneman e Frederick). I bias cognitivi, invece, sono euristiche inefficaci, pregiudizi astratti che non si generano su dati di realtà, ma si acquisiscono a priori senza critica o giudizio, fondati, su percezioni errate o deformate, su pregiudizi e ideologie. Di seguito alcuni dei BIAS più comuni.Secondo Daniel Kahneman occorre impegnarsi a costruire una "società critica", nella quale ci siano "osservatori critici". 

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Artificial Intelligence, Deep Learning and Big Data: a New Start?

When I was a university student I was terribly naïve about the power of Artificial Neural Networks. While I was trying to replicate portions of human cognition by practicing with neural networks and non-linear dynamics, I stumbled into a crazy idea: the time series of the winning results of a multimillion lottery are apparently chaotic; what if - say because of the mechanical constraints of the devices that pick up the numbers or for any other reason in the universe- there was a hidden statistical pattern behind that apparent chaos? If that was the case, then anyone endowed with the ability to deal with the chaotic nature of that huge amount of data could probably get an insight into that hidden mechanism. As it is well known, recurrent neural networks are non-linear dynamical systems able to learn time series. That was sufficient for me to start encode thousands of winning series of a popular lottery into vectors to feed into my latest Recurrent Multi-layer Neural Network experiment to make it learn to predict, at least at a degree, the next winning one. I was trying to hack the lottery. Wow! You may say: are you crazy? Probably, I was. And I was even crazier because of having implemented the whole stuff in Java and expected it to run on my Intel 486 Windows PC! I know you want to know how it ended. Let it suffice to say that I did not become a millionaire. Jokes aside, the very high dimensionality of data and the computational resources needed to carry out the training of the network largely exceeded my computer's capability that finally, after two days of computation, got stuck. That was the very first time I had a joint meeting with what we currently call "Deep Learning" and "Big Data".
https://www.linkedin.com/pulse/artificial-intelligence-deep-learning-big-data-new-start-terenzi
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Managing deep uncertainty: Exploratory modeling, adaptive plans and joint sense making

Community member post by Jan Kwakkel How can decision making on complex systems come to grips with irreducible, or deep, uncertainty? Such uncertainty has three sources: Intrinsic limits to predictability in complex systems. A variety of stakeholders with different perspectives on what the system is and what problem needs to be solved. Complex systems are…
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Multi-Agent Systems and Simulation: A Survey from the Agent Community's Perspective 

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VALORE E AUTONOMIA DELL’IMPROVVISAZIONE. TRA ARTI E PRATICHE

Abstract In this paper I will accept Georg Bertram’s criticism against what he calls the “autonomist paradigm” in philosophy of art and I will follow his theoretical suggestion: a coherent, informed, and accomplished philosophy of art should consider not only the specific nature of art, but also its value for the human practices and as one of the human practices. However, I will show the connection between human practices and art in a different, although related, way. Instead of beginning from a reflection focused on art, I will rather move from the human practices, showing that “art” may be a particular way to look at and to develop human practices. I shall argue that the theoretical link between human practices and art can be provided by the notion of improvisation. Improvisation is not only a particular artistic technique. Rather, improvisation can be more generally understood as the paradigm of art, in the interesting sense, defended by Bertram, of incorporating and showing in a genetic way, on the one hand, the autonomous art specificity and, on the other hand, the value of art, that is, the link between human practices and art as a specific human practice. In this sense, art (as specific human practice) both derives from and is a particular way to improvise (upon) the human practices, i.e. to develop them in ways that can be valuable (both in general and artistically or aesthetically). Accordingly, improvisation as a specific artistic procedure will be understood as that kind of artistic production in which the human practice underlying art comes, as it were, to the fore.
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Constraint-Directed Improvisation for Complex Domains

Abstract Current approaches to reactive planning are limited in their ability to perform well in domains characterized by complexity and significant variability, their ability to perform in domains with which they are less than completely familiar, and by their reliance on local information for decision making. In this paper, we present a novel architecture known as Waffler, based on constraint-directed reasoning. This architecture allows an agent to perform in complex, variable domains in real time by improvising on a routine method of accomplishing an activity using the background knowledge from which the agent's routine is derived. Agents employing this approach can follow a routine in the face of uncertainty and variability, and can apply a routine in a situation with novel aspects, satisficing to the degree that time is available. This paper describes the Waffler architecture's basis in constraint-directed reasoning, it's knowledge structures and processing mechanisms, and an implementation in a simulated environment.
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We need to shine more light on algorithms so they can help reduce bias, not perpetuate it

We need to shine more light on algorithms so they can help reduce bias, not perpetuate it | Bounded Rationality and Beyond | Scoop.it

Courts, banks, and other institutions are using automated data analysis systems to make decisions about your life. Let’s not leave it up to the algorithm makers to decide whether they’re doing it appropriately. ProPublica, a Pulitzer Prize–winning nonprofit news organization, had analyzed risk assessment software known as COMPAS. It is being used to forecast which criminals are most likely to ­reoffend. Guided by such forecasts, judges in courtrooms throughout the United States make decisions about the future of defendants and convicts, determining everything from bail amounts to sentences. When ProPublica compared COMPAS’s risk assessments for more than 10,000 people arrested in one Florida county with how often those people actually went on to reoffend, it discovered that the algorithm “correctly predicted recidivism for black and white defendants at roughly the same rate.” But when the algorithm was wrong, it was wrong in different ways for blacks and whites. Specifically, “blacks are almost twice as likely as whites to be labeled a higher risk but not actually re-offend.” And COMPAS tended to make the opposite mistake with whites: “They are much more likely than blacks to be labeled lower risk but go on to commit other crimes.” Things reviewed “Machine Bias” ProPublica, May 23, 2016 “COMPAS Risk Scales: Demonstrating Accuracy Equity and Predictive Parity” Northpointe, July 8, 2016 “Technical Response to Northpointe” ProPublica, July 29, 2016 “False Positives, False Negatives, and False Analyses: A Rejoinder to ‘Machine Bias’” Anthony Flores, Christopher Lowenkamp, and Kristin Bechtel August 10, 2016 Courts, banks, and other institutions are using automated data analysis systems to make decisions about your life. Let’s not leave it up to the algorithm makers to decide whether they’re doing it appropriately. 

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Miklos Szilagyi's curator insight, August 14, 2:59 AM
Well, and it's only an example... we are guided/controlled/selected/rejected/etc. acc. to results of algorythms on many (if not all of the...) areas... i.e. we are finally submitted to programmers/coders final logic/thinking... if we are not able to check, monitor & veto on some meta-levels the working of these algirithms (are we guys?!), then, well, one thing remains: to pray...
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An algorithm trained on emoji knows when you’re being sarcastic on Twitter

An algorithm trained on emoji knows when you’re being sarcastic on Twitter | Bounded Rationality and Beyond | Scoop.it

Understanding sarcasm could help AI fight racism, abuse, and harassment. The researchers originally aimed to develop a system capable of detecting racist posts on Twitter. But they soon realized that the meaning of many messages couldn’t be properly understood without some understanding of sarcasm. The algorithm uses deep learning, a popular machine-learning technique that relies on training a very large simulated neural network to recognize subtle patterns using a large amount of data. The secret to training this algorithm was that many tweets already use something like a labeling system for emotional content: emoji. Once they took advantage of this to help the system read tweets for emotion in general, the researchers had a head start in teaching it to recognize sarcasm.

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Miklos Szilagyi's curator insight, August 14, 2:54 AM
Wow... this algorithm is better to spot sarcasm, i.e. complex emotion in written text through emojis than the control human test group... wow...