1. Humans use mental shortcuts called heuristics to make decisions quickly with limited time, information, and computational resources, though heuristics can sometimes lead to errors.
2. Heuristics evolved in social environments and are adapted for solving social problems. Examples include anchoring biases and representativeness heuristics.
3. More recent views see heuristics not as weaknesses but as adaptive, ecologically rational strategies that exploit environmental structures and our cognitive capacities. Heuristics can outperform more complex algorithms in real-world settings.
4. Heuristics are shaped by natural and sexual selection pressures. Our large
The document discusses heuristics, which are mental shortcuts or rules of thumb that people use to make decisions quickly and with limited information. Some examples of heuristics discussed include anchoring, representativeness, and recognition heuristics. The document suggests that while heuristics can lead to errors, they have evolved because making fully rational decisions would require too many computational resources, and heuristics allow for faster decisions under real-world constraints of time and information.
The document discusses developing models of cognitive behavior from psychology to create self-awareness in information and communication technology (ICT). The goals are to 1) identify a robust psychological basis for self-awareness in ICT and 2) exploit this basis in a content-centric Internet. This would involve applying cognitive processes from the human brain, like understanding and inference, to provide intelligent content acquisition. The approach is to define key psychological principles and embed them in technology to change behavior through self-awareness.
This document discusses varieties of self-awareness and their uses in natural and artificial systems. It proposes a conceptual framework for metacognition and natural cognition. The document contains slides for presentations on this topic, including:
- Discussing how to analyze requirements by examining natural and artificial systems to understand design discontinuities.
- Explaining how environments can have agent-relative structure that produces varied information processing demands.
- Outlining a conceptual framework that includes reactive and deliberative architectures in natural systems, with different layers providing varieties of self-awareness.
The document discusses ensemble-oriented programming and self-adaptive systems. It provides an overview of the E-Vehicle case study that will be used to demonstrate a Service Component Ensemble Language (SCEL) and its runtime framework in Java (jRESP). The case study involves coordination between users, vehicles, and parking lots to satisfy transportation needs and optimize resource allocation.
This document summarizes a presentation on self-adaptation and self-awareness with a focus on reflective Russian dolls. It defines adaptation as the run-time modification of control data. It presents an approach using reflective Russian dolls to support formal techniques for adaptation and self-awareness. This involves using logical reflection and wrapping techniques to represent adaptive systems as towers of reflections. The presentation discusses using Maude to formally model autonomic managers and adaptive systems.
The document discusses self-awareness at the hardware/software interface. It describes how reconfigurable hardware allows the adaptation of hardware at runtime. The EPiCS approach uses proprioceptive computing to enable compute nodes to adapt to changing system states through self-awareness and self-expression at the hardware/software interface. This involves using multithreading as a unified programming model for heterogeneous multi-cores consisting of CPU, FPGA, and monitoring cores.
This document provides an introduction to complex systems and agent-based modeling. It discusses what complex systems are, including examples ranging from simple systems of a few agents to more sophisticated systems involving many agents. Complex systems are characterized as having emergent behaviors that arise from the interactions of the agents following simple rules, without any centralized control. The document also provides examples of complex systems in nature, such as pattern formation, neural networks, swarm intelligence in insect colonies, collective motion of flocking and schooling, and social biological systems.
The document discusses heuristics, which are mental shortcuts or rules of thumb that people use to make decisions quickly and with limited information. Some examples of heuristics discussed include anchoring, representativeness, and recognition heuristics. The document suggests that while heuristics can lead to errors, they have evolved because making fully rational decisions would require too many computational resources, and heuristics allow for faster decisions under real-world constraints of time and information.
The document discusses developing models of cognitive behavior from psychology to create self-awareness in information and communication technology (ICT). The goals are to 1) identify a robust psychological basis for self-awareness in ICT and 2) exploit this basis in a content-centric Internet. This would involve applying cognitive processes from the human brain, like understanding and inference, to provide intelligent content acquisition. The approach is to define key psychological principles and embed them in technology to change behavior through self-awareness.
This document discusses varieties of self-awareness and their uses in natural and artificial systems. It proposes a conceptual framework for metacognition and natural cognition. The document contains slides for presentations on this topic, including:
- Discussing how to analyze requirements by examining natural and artificial systems to understand design discontinuities.
- Explaining how environments can have agent-relative structure that produces varied information processing demands.
- Outlining a conceptual framework that includes reactive and deliberative architectures in natural systems, with different layers providing varieties of self-awareness.
The document discusses ensemble-oriented programming and self-adaptive systems. It provides an overview of the E-Vehicle case study that will be used to demonstrate a Service Component Ensemble Language (SCEL) and its runtime framework in Java (jRESP). The case study involves coordination between users, vehicles, and parking lots to satisfy transportation needs and optimize resource allocation.
This document summarizes a presentation on self-adaptation and self-awareness with a focus on reflective Russian dolls. It defines adaptation as the run-time modification of control data. It presents an approach using reflective Russian dolls to support formal techniques for adaptation and self-awareness. This involves using logical reflection and wrapping techniques to represent adaptive systems as towers of reflections. The presentation discusses using Maude to formally model autonomic managers and adaptive systems.
The document discusses self-awareness at the hardware/software interface. It describes how reconfigurable hardware allows the adaptation of hardware at runtime. The EPiCS approach uses proprioceptive computing to enable compute nodes to adapt to changing system states through self-awareness and self-expression at the hardware/software interface. This involves using multithreading as a unified programming model for heterogeneous multi-cores consisting of CPU, FPGA, and monitoring cores.
This document provides an introduction to complex systems and agent-based modeling. It discusses what complex systems are, including examples ranging from simple systems of a few agents to more sophisticated systems involving many agents. Complex systems are characterized as having emergent behaviors that arise from the interactions of the agents following simple rules, without any centralized control. The document also provides examples of complex systems in nature, such as pattern formation, neural networks, swarm intelligence in insect colonies, collective motion of flocking and schooling, and social biological systems.
The document discusses the CEEDs project, which aims to exploit implicit processing to help humans make sense of large, complex datasets. The CEEDs project develops new sensors and technologies to measure people's implicit reactions to visualizations of big data, even when they are not consciously aware of their reactions. A "Sentient Agent" model uses measures like heart rate, skin conductance and eye tracking to infer user engagement and guide them through the data. The document provides examples of applying CEEDs technology in archaeology and neuroscience to help classify artifacts, analyze ancient cities, and explore brain connectivity networks.
1. Visual illusions (also called optical illusions) show that the br.pdfmohammedfootwear
1. Visual illusions (also called optical illusions) show that the brain can be readily fooled. (1
page)
a. What insights do visual illusions offer us regarding the workings of the brain?
b. How can these insights be extended to explain the brain’s role in decision making?
2. One of the most intriguing questions we face when studying how the brain functions in
making decisions is: What role does the unconscious brain play in decision making? (1-2 pages
total)
a. What are the strengths and limitations of the conscious brain in decision making?
b. How can the unconscious brain contribute to an individual’s decision making capability?
c. What do anecdotal accounts—such as those provided by Malcolm Gladwell in Blink—tell us
about the role of the unconscious brain in decision making?
d. What do experimental studies—such as those carried out by Wilson and Dijksterhuis—tell
us?
e. Based on the experimental studies reported in Framing Decisions, what are the merits of
making on-the-spot decisions vs. decisions after substantial deliberation (e.g, by sleeping on a
decision)?
3. Page 104 of Framing Decisions identifies four sets of questions decisions makers need to
address when making decisions of consequence in order to surface potential moral hazard
situations. Explain the rationale underlying each question. If you ask these questions when
deliberating on decisions of consequence, how can you improve the quality of your decision
making? (1-2 pages)
Solution
Answer-1
a. When you look at something, what you’re really seeing is the light that bounced off of it and
entered your eye, which converts the light into electrical impulses that your brain can turn into an
image you can use. The process that takes about a tenth of a second but your eyes receive a
constant stream of light, an incredible amount of information, so it’s really difficult for your
brain to try to focus on everything at once. It would be like trying to take a sip of water from a
firehose. So your brain takes shortcuts, simplifying what you see to help you concentrate on
what’s important, which helps compensate for your brain’s tenth-of-a-second processing lag.
This trait helped early humans survive encounters with fast predators – or at the very least avoid
running into obstacles like trees.
b. a sample of three decision errors. First, the default effectoccurs when people end up
“choosing” different options when allowed not to choose at all, i.e., when a lack of any active
selection returns the default. Impressively, countries that allow individuals to decline being a
potential organ donor have far greater donor pools than countries that allow individuals to
decline not being a potential donor (Johnson & Goldstein, 2003). Second, Dan reports that
physicians are more likely to pull a patient back from scheduled surgery when they discover that
they forgot to test the efficacy of one drug, than when they notice that they overlooked two
drugs. In the latter case, the physicians would need t.
Rational Choice Theory Of Criminal Behavior EssayAlison Hall
Rational choice theory posits that individuals make rational decisions to commit crimes by weighing the costs and benefits. When the benefits of a criminal act outweigh the costs, individuals will choose to commit that crime. The theory assumes people act rationally and in their own self-interest. It has been applied to explain various crimes like robbery, drug use, and white-collar offenses. The theory also has limitations and faces criticism for oversimplifying criminal decision-making.
Human Intelligence Source Analysis
- Human Intelligence relies on personal connections with sources of information. Close relationships allow access to insights not available through other forms of intelligence, such as decision-making processes and moral obligations.
- Developed contacts can bypass security through means like access badges or ID cards.
- While powerful, Human Intelligence is also susceptible to counterintelligence operations and deception due to the close personal involvement between agents and sources.
This document discusses the concept of psychohistory proposed by Isaac Asimov - the idea that history and human social behavior could be mathematically predicted and modeled on a large scale. It examines whether such global simulations are possible given our current understanding of human psychology, behavior, and social systems. While advances in computing power and data collection have improved our ability to model populations, accurately simulating individual human behavior remains challenging due to factors like noise, complexity, self-organization, and limited psychological knowledge.
The document discusses various cognitive biases and heuristics that influence human decision-making, such as the planning fallacy in which people underestimate costs and overestimate benefits, and optimism bias which can motivate action but also lead to false beliefs. It also examines loss aversion bias and how optimism can help protect against the paralyzing effects of fearing losses more than valuing gains. A number of heuristics are explored, including the affect heuristic where emotional reactions can drive behavior over cognitive risk assessments.
This document discusses the Rational Choice model and its assumptions about human decision-making. While the model assumes humans make rational decisions to maximize utility, research in behavioral economics has found ways in which human psychology can lead to irrational decisions. Specifically, Daniel Kahneman's research on System 1 and System 2 thinking shows how quick, automatic judgments (System 1) can be biased and lead to errors. However, the document argues that while behavioral findings show limitations, the Rational Choice model still provides a useful framework for understanding aggregate human decision-making when assumptions are not taken as absolute. It should not be abandoned, as it generally predicts outcomes even if some individual decisions depart from strict rationality.
This document discusses three central ideas in the intellectual history of the mind: logic, probability, and heuristics. It explains that while logic and probability have been formalized, heuristics have not yet received the same treatment. The author advocates for developing formal models of heuristics to better understand when and why they work. Specifically, such models could help identify the environments ("ecological rationality") in which specific heuristics succeed or fail. This would address misunderstandings about heuristics and help design effective decision-making strategies.
Nudges are subtle ways of influencing consumer decisions without limiting choices. Choice architects can design contexts that steer behaviors through defaults, social proof, and salience. While nudges can increase organ donation and retirement savings rates, they may also undermine autonomy and evade important issues. Effective nudges require understanding behavior in context rather than one-size-fits-all solutions.
Conferencia de Adolf Tobeña sobre Neurociencia y Libertad, realizada en Tarragona el 30/10/2010 dentro del ciclo organizado por cultura 3.0 "Verdad, belleza, bien, felicidad, libertad y justicia a la luz de la ciencia moderna".
Our study found that applying a performance-based “expert” weighted method to the crowd improves the crowds’ wisdom, measured by crowd accuracy. This finding contrasts previous research that was not able to find a significant improvement in accuracy by applying weighted methods. This research indicates that in order to optimise crowdsourcing, experts within the crowd should be given higher weighting compared to non-experts.
Security Is Like An Onion, That's Why It Makes You CryMichele Chubirka
Why is the security industry so full of fail? We spend millions of dollars on firewalls, IPS, IDS, DLP, professional penetration tests and assessments, vulnerability and compliance tools and at the end of the day, the weakest link is the user and his or her inability to make the right choices. It's enough to make a security engineer cry. The one thing you can depend upon in an enterprise is that many of our users, even with training, will still make the wrong choices. They still click on links they shouldn't, respond to phishing scams, open documents without thinking, post too much information on Twitter and Facebook, use their pet's name as passwords, etc'. But what if this isn't because users hate us or are too stupid? What if all our complaints about not being heard and our instructions regarding the best security practices have more to do with our failure to understand modern neuroscience and the human mind's resistance to change?
Herbert Simon argues that our task is not to predict the future, but to shape it. He outlines three conditions for an acceptable future: 1) living sustainably within Earth's limits, 2) fair sharing of resources, and 3) reducing divisions between groups. Technology provides both opportunities and risks, and it is up to us to guide technology's development and use to foster beneficial outcomes for humanity. Computers in particular can help us understand human cognition and design a sustainable future by modeling minds and societies.
This document discusses several key ideas and debates within the human sciences. It compares the human sciences, history, and natural sciences, noting that while human sciences seek generalizations like the natural sciences, studying humans is more complex due to changing societies and individuals. It also discusses the debate between naturalist and interpretivist approaches, and some of the challenges of achieving certainty in the human sciences, such as the complexity of human behavior and societies. Key ideas discussed in more depth include the distinction between correlation and causation, the concept of path dependence, the nature vs nurture debate, and issues around determinism and free will.
In recent decades, psychologists and economists have cataloged the ways in which human behavior deviates
from economic theory.1 They have done this mostly through experiments and observation. Daniel Kahneman
and Amos Tversky, psychologists who formalized this research, showed that individuals use heuristics, or rules
of thumb, to make their judgments. These heuristics lead to biases when compared to normative economic
behavior.2 For example, people generally place too much weight on information that is available to their minds,
often associated with an event that is vivid or recent, and overestimate the probability of a similar event
occurring again.
The Homunculus Problem: Why You Will Lose the Battle of BYODMichele Chubirka
BYOD, it's the new enterprise Boogie Man, striking fear into the heart of security professionals everywhere. We think this is a simple issue of policy, but if a recent study is correct and 20-somethings will risk their jobs to use their own devices, it's clear there's more going on. One explanation for the attachment to our smartphones and tablets can be found in neuroscience.
Studies show that texting, Twitter and Facebook usage activate the same addictive patterns in the brain as heroin and cigarettes. With advances in neuroengineering and brain computer interfaces, it sounds as if we're arguing with the inevitable, ultimate BYOD. Science continues to make advancements toward using technology to overcome the limitations of paralysis or to repair the damaged areas of the brain. Many of these devices will be wireless and in our enterprises. Parag Khanna and Ayesha Khanna in a recent TED book said we've entered a Hybrid Age, "...a new sociotechnical era that is unfolding as technologies merge with each other and humans merge with technology..." The BYOD cat is out of the bag, the barbarians are at the gates. Therefore, the answer to BYOD cannot be, “No,” but a qualified “Yes, and....”
Emotional influences led the author to make a suboptimal financial decision to go to Disneyland instead of working and earning £218. The author spent £150 on the trip, resulting in a potential £368 loss. The decision was influenced by heuristics like availability bias, based on positive memories of Disney. More time for deliberation using System 2 thinking could have avoided this misjudgment by considering long-term financial implications rather than short-term emotions. Awareness of psychological factors influencing judgment is key to making informed decisions.
Robots working in swarms need to be self-aware to adapt their behavior based on task performance and collective behavior emerges. Self-aware computing systems could help manage distributed energy production and consumption in smart grids. Data and services could manage themselves in an "ecosystem" through decentralized algorithms. Human cognitive processes like inference could help systems manage internet content by acquiring new content and filtering existing content. Self-aware electric vehicles could communicate to improve reliability, adaptability, and predictability through cooperation. Science clouds use self-aware computing to manage distributed notebooks, servers and virtual machines.
This document discusses the problems of safety and ethics in autonomous systems like robots. Ensuring safe behavior is difficult when robots operate in unpredictable human environments, and they pose ethical challenges if capable of harming humans, inducing emotional responses, appearing intelligent without being so, or causing harm without a responsible party. The author proposes that internal models allowing robots to predict action consequences and check them against safety and ethical rules could enable truly safe and ethical autonomous robots. Self-awareness through internal modeling may be needed to guarantee safety for robots and other autonomous systems working in unknown environments.
More Related Content
Similar to Introduction to human heuristics by Franco Bagnoli
The document discusses the CEEDs project, which aims to exploit implicit processing to help humans make sense of large, complex datasets. The CEEDs project develops new sensors and technologies to measure people's implicit reactions to visualizations of big data, even when they are not consciously aware of their reactions. A "Sentient Agent" model uses measures like heart rate, skin conductance and eye tracking to infer user engagement and guide them through the data. The document provides examples of applying CEEDs technology in archaeology and neuroscience to help classify artifacts, analyze ancient cities, and explore brain connectivity networks.
1. Visual illusions (also called optical illusions) show that the br.pdfmohammedfootwear
1. Visual illusions (also called optical illusions) show that the brain can be readily fooled. (1
page)
a. What insights do visual illusions offer us regarding the workings of the brain?
b. How can these insights be extended to explain the brain’s role in decision making?
2. One of the most intriguing questions we face when studying how the brain functions in
making decisions is: What role does the unconscious brain play in decision making? (1-2 pages
total)
a. What are the strengths and limitations of the conscious brain in decision making?
b. How can the unconscious brain contribute to an individual’s decision making capability?
c. What do anecdotal accounts—such as those provided by Malcolm Gladwell in Blink—tell us
about the role of the unconscious brain in decision making?
d. What do experimental studies—such as those carried out by Wilson and Dijksterhuis—tell
us?
e. Based on the experimental studies reported in Framing Decisions, what are the merits of
making on-the-spot decisions vs. decisions after substantial deliberation (e.g, by sleeping on a
decision)?
3. Page 104 of Framing Decisions identifies four sets of questions decisions makers need to
address when making decisions of consequence in order to surface potential moral hazard
situations. Explain the rationale underlying each question. If you ask these questions when
deliberating on decisions of consequence, how can you improve the quality of your decision
making? (1-2 pages)
Solution
Answer-1
a. When you look at something, what you’re really seeing is the light that bounced off of it and
entered your eye, which converts the light into electrical impulses that your brain can turn into an
image you can use. The process that takes about a tenth of a second but your eyes receive a
constant stream of light, an incredible amount of information, so it’s really difficult for your
brain to try to focus on everything at once. It would be like trying to take a sip of water from a
firehose. So your brain takes shortcuts, simplifying what you see to help you concentrate on
what’s important, which helps compensate for your brain’s tenth-of-a-second processing lag.
This trait helped early humans survive encounters with fast predators – or at the very least avoid
running into obstacles like trees.
b. a sample of three decision errors. First, the default effectoccurs when people end up
“choosing” different options when allowed not to choose at all, i.e., when a lack of any active
selection returns the default. Impressively, countries that allow individuals to decline being a
potential organ donor have far greater donor pools than countries that allow individuals to
decline not being a potential donor (Johnson & Goldstein, 2003). Second, Dan reports that
physicians are more likely to pull a patient back from scheduled surgery when they discover that
they forgot to test the efficacy of one drug, than when they notice that they overlooked two
drugs. In the latter case, the physicians would need t.
Rational Choice Theory Of Criminal Behavior EssayAlison Hall
Rational choice theory posits that individuals make rational decisions to commit crimes by weighing the costs and benefits. When the benefits of a criminal act outweigh the costs, individuals will choose to commit that crime. The theory assumes people act rationally and in their own self-interest. It has been applied to explain various crimes like robbery, drug use, and white-collar offenses. The theory also has limitations and faces criticism for oversimplifying criminal decision-making.
Human Intelligence Source Analysis
- Human Intelligence relies on personal connections with sources of information. Close relationships allow access to insights not available through other forms of intelligence, such as decision-making processes and moral obligations.
- Developed contacts can bypass security through means like access badges or ID cards.
- While powerful, Human Intelligence is also susceptible to counterintelligence operations and deception due to the close personal involvement between agents and sources.
This document discusses the concept of psychohistory proposed by Isaac Asimov - the idea that history and human social behavior could be mathematically predicted and modeled on a large scale. It examines whether such global simulations are possible given our current understanding of human psychology, behavior, and social systems. While advances in computing power and data collection have improved our ability to model populations, accurately simulating individual human behavior remains challenging due to factors like noise, complexity, self-organization, and limited psychological knowledge.
The document discusses various cognitive biases and heuristics that influence human decision-making, such as the planning fallacy in which people underestimate costs and overestimate benefits, and optimism bias which can motivate action but also lead to false beliefs. It also examines loss aversion bias and how optimism can help protect against the paralyzing effects of fearing losses more than valuing gains. A number of heuristics are explored, including the affect heuristic where emotional reactions can drive behavior over cognitive risk assessments.
This document discusses the Rational Choice model and its assumptions about human decision-making. While the model assumes humans make rational decisions to maximize utility, research in behavioral economics has found ways in which human psychology can lead to irrational decisions. Specifically, Daniel Kahneman's research on System 1 and System 2 thinking shows how quick, automatic judgments (System 1) can be biased and lead to errors. However, the document argues that while behavioral findings show limitations, the Rational Choice model still provides a useful framework for understanding aggregate human decision-making when assumptions are not taken as absolute. It should not be abandoned, as it generally predicts outcomes even if some individual decisions depart from strict rationality.
This document discusses three central ideas in the intellectual history of the mind: logic, probability, and heuristics. It explains that while logic and probability have been formalized, heuristics have not yet received the same treatment. The author advocates for developing formal models of heuristics to better understand when and why they work. Specifically, such models could help identify the environments ("ecological rationality") in which specific heuristics succeed or fail. This would address misunderstandings about heuristics and help design effective decision-making strategies.
Nudges are subtle ways of influencing consumer decisions without limiting choices. Choice architects can design contexts that steer behaviors through defaults, social proof, and salience. While nudges can increase organ donation and retirement savings rates, they may also undermine autonomy and evade important issues. Effective nudges require understanding behavior in context rather than one-size-fits-all solutions.
Conferencia de Adolf Tobeña sobre Neurociencia y Libertad, realizada en Tarragona el 30/10/2010 dentro del ciclo organizado por cultura 3.0 "Verdad, belleza, bien, felicidad, libertad y justicia a la luz de la ciencia moderna".
Our study found that applying a performance-based “expert” weighted method to the crowd improves the crowds’ wisdom, measured by crowd accuracy. This finding contrasts previous research that was not able to find a significant improvement in accuracy by applying weighted methods. This research indicates that in order to optimise crowdsourcing, experts within the crowd should be given higher weighting compared to non-experts.
Security Is Like An Onion, That's Why It Makes You CryMichele Chubirka
Why is the security industry so full of fail? We spend millions of dollars on firewalls, IPS, IDS, DLP, professional penetration tests and assessments, vulnerability and compliance tools and at the end of the day, the weakest link is the user and his or her inability to make the right choices. It's enough to make a security engineer cry. The one thing you can depend upon in an enterprise is that many of our users, even with training, will still make the wrong choices. They still click on links they shouldn't, respond to phishing scams, open documents without thinking, post too much information on Twitter and Facebook, use their pet's name as passwords, etc'. But what if this isn't because users hate us or are too stupid? What if all our complaints about not being heard and our instructions regarding the best security practices have more to do with our failure to understand modern neuroscience and the human mind's resistance to change?
Herbert Simon argues that our task is not to predict the future, but to shape it. He outlines three conditions for an acceptable future: 1) living sustainably within Earth's limits, 2) fair sharing of resources, and 3) reducing divisions between groups. Technology provides both opportunities and risks, and it is up to us to guide technology's development and use to foster beneficial outcomes for humanity. Computers in particular can help us understand human cognition and design a sustainable future by modeling minds and societies.
This document discusses several key ideas and debates within the human sciences. It compares the human sciences, history, and natural sciences, noting that while human sciences seek generalizations like the natural sciences, studying humans is more complex due to changing societies and individuals. It also discusses the debate between naturalist and interpretivist approaches, and some of the challenges of achieving certainty in the human sciences, such as the complexity of human behavior and societies. Key ideas discussed in more depth include the distinction between correlation and causation, the concept of path dependence, the nature vs nurture debate, and issues around determinism and free will.
In recent decades, psychologists and economists have cataloged the ways in which human behavior deviates
from economic theory.1 They have done this mostly through experiments and observation. Daniel Kahneman
and Amos Tversky, psychologists who formalized this research, showed that individuals use heuristics, or rules
of thumb, to make their judgments. These heuristics lead to biases when compared to normative economic
behavior.2 For example, people generally place too much weight on information that is available to their minds,
often associated with an event that is vivid or recent, and overestimate the probability of a similar event
occurring again.
The Homunculus Problem: Why You Will Lose the Battle of BYODMichele Chubirka
BYOD, it's the new enterprise Boogie Man, striking fear into the heart of security professionals everywhere. We think this is a simple issue of policy, but if a recent study is correct and 20-somethings will risk their jobs to use their own devices, it's clear there's more going on. One explanation for the attachment to our smartphones and tablets can be found in neuroscience.
Studies show that texting, Twitter and Facebook usage activate the same addictive patterns in the brain as heroin and cigarettes. With advances in neuroengineering and brain computer interfaces, it sounds as if we're arguing with the inevitable, ultimate BYOD. Science continues to make advancements toward using technology to overcome the limitations of paralysis or to repair the damaged areas of the brain. Many of these devices will be wireless and in our enterprises. Parag Khanna and Ayesha Khanna in a recent TED book said we've entered a Hybrid Age, "...a new sociotechnical era that is unfolding as technologies merge with each other and humans merge with technology..." The BYOD cat is out of the bag, the barbarians are at the gates. Therefore, the answer to BYOD cannot be, “No,” but a qualified “Yes, and....”
Emotional influences led the author to make a suboptimal financial decision to go to Disneyland instead of working and earning £218. The author spent £150 on the trip, resulting in a potential £368 loss. The decision was influenced by heuristics like availability bias, based on positive memories of Disney. More time for deliberation using System 2 thinking could have avoided this misjudgment by considering long-term financial implications rather than short-term emotions. Awareness of psychological factors influencing judgment is key to making informed decisions.
Robots working in swarms need to be self-aware to adapt their behavior based on task performance and collective behavior emerges. Self-aware computing systems could help manage distributed energy production and consumption in smart grids. Data and services could manage themselves in an "ecosystem" through decentralized algorithms. Human cognitive processes like inference could help systems manage internet content by acquiring new content and filtering existing content. Self-aware electric vehicles could communicate to improve reliability, adaptability, and predictability through cooperation. Science clouds use self-aware computing to manage distributed notebooks, servers and virtual machines.
This document discusses the problems of safety and ethics in autonomous systems like robots. Ensuring safe behavior is difficult when robots operate in unpredictable human environments, and they pose ethical challenges if capable of harming humans, inducing emotional responses, appearing intelligent without being so, or causing harm without a responsible party. The author proposes that internal models allowing robots to predict action consequences and check them against safety and ethical rules could enable truly safe and ethical autonomous robots. Self-awareness through internal modeling may be needed to guarantee safety for robots and other autonomous systems working in unknown environments.
This document discusses design patterns for autonomic systems. It begins by explaining what design patterns are and how they allow common solutions to recurring problems to be reused, saving time. It then discusses how patterns are described and can be composed to solve different problems. The document outlines several bio-inspired design patterns for autonomic computing systems, including spreading, aggregation, evaporation, and repulsion. It concludes by discussing a taxonomy for classifying patterns according to the component and ensemble levels in an autonomic system.
This document provides an introduction to modeling and analyzing autonomic systems. It discusses modeling autonomic systems using the SOTA/GEM framework for requirements specification and the SCEL modeling language. It then presents a case study of modeling a swarm of garbage collecting robots. Key steps include modeling goals and requirements, selecting adaptation patterns, modeling the robot behavior and interactions in SCEL, and validating requirements through quantitative analysis using techniques like CTMC and ODE models. The document outlines the iterative design time and runtime engineering process for autonomic systems using these techniques.
This document discusses morphogenetic engineering, which aims to design decentralized systems capable of developing elaborate morphologies without central planning. It covers three main topics:
1) Engineering and control of self-organization, which involves fostering and guiding complex systems through their elements.
2) Morphogenetic engineering, which explores artificial design of systems that can develop architectures like those seen in biology, with heterogeneous and hierarchical structures emerging from self-organization.
3) Embryomorphic engineering, which takes inspiration from biological morphogenesis and development, aiming to design multi-agent models that can undergo evolution and development like living organisms. The goal is to better understand novelty in evolution by studying emergence at the microscopic, agent level.
The document discusses autonomic multi-agent systems and self-awareness. It covers:
1) The objectives of understanding fundamental properties of autonomic systems and how agents can use environmental awareness for self-organization.
2) An overview of multi-agent systems, autonomic systems, and representative approaches like dynamic norm-governed systems.
3) How awareness can enable self-healing through maintaining congruence between rules and system state.
This document discusses common features of complex systems and networks. It notes that complex systems generally have a large number of elements that follow individual behavior rules and interact locally. The systems exhibit node and link diversity and dynamics. They can display hierarchy across different levels and heterogeneity. Complex networks form the backbone of complex systems. Network structure influences function and vice versa. Three key metrics to characterize networks are described - average path length, degree distribution, and clustering coefficient. Different types of networks, including random, regular, small-world and scale-free are also discussed.
This document discusses self-awareness in psychology and proposes a framework for computational self-awareness. It defines different types of self-awareness, such as implicit/explicit and private/public. It also outlines levels of self-awareness ranging from stimulus awareness to meta-self-awareness. Finally, it proposes applying these concepts to computing by defining private and public computational self-awareness and levels that could emerge from interactions between components.
The document provides an outline for a presentation on self-awareness in autonomic systems. It discusses introductory examples of robot swarms, science clouds, and cooperative electric vehicles. It then motivates the need for awareness in complex distributed systems like communication and power networks. Existing research projects exploring self-awareness concepts are summarized, including ASCENS, CoCoRo, EPiCS, RECOGNITION, SAPERE, and SYMBRION. Nature-inspired examples of self-aware behaviors in flocking, ant foraging, quorum sensing, chemotaxis, morphogenesis, and gossiping are presented. Finally, awareness properties in biological systems like the immune system are discussed.
This document summarizes several research projects related to autonomic and self-aware systems. It discusses proprioceptive systems like EPiCS which aim to develop self-aware and self-expressive computing systems. It also discusses swarm robotics projects like SYMBRION that develop robotic swarms capable of self-organization. Data management projects like SAPERE and RECOGNITION seek to develop self-aware techniques for acquiring and managing large amounts of data and content.
Simulation tools can help understand natural systems and develop self-aware systems. Existing simulators like Repast and The ONE have advantages but lack certain features. The CoSMoS method structures simulation development through domain, platform, and results models to help ensure simulations accurately represent domains. Simulations aid controller design for systems like underwater robots, though the "reality gap" between simulation and reality requires attention.
The document discusses awareness in autonomous systems. It covers general properties of self-awareness like perception and collectivity. It also discusses the short-term impacts of self-awareness like safety and sustainability and long-term open issues. Key aspects of self-awareness are levels ranging from ecological to conceptual awareness. Distributed emergence of self-awareness is possible through collective systems though parts exhibit less awareness. Internal models are important for self-aware systems to represent themselves and environments to test possibilities.
This document discusses self-awareness in autonomous systems and provides examples. It defines autonomic systems as self-governing systems that can operate without external direction in complex environments. Examples discussed include robot swarms, science clouds, and cooperative electric vehicles. The motivation for self-awareness in information and communication technology systems is that as systems become more distributed and complex, they require mechanisms to manage and organize themselves. Existing self-aware systems in nature that provide inspiration include flocking behavior in animals and ant foraging behavior through decentralized coordination.
This document discusses robot swarms and swarm robotics. It introduces marXbot, a miniature mobile robot with various sensors that can dock with other robots. It discusses problems with swarm robotics like noise and uncertainty. It then covers using action logics and Markov decision processes to model probabilistic behavior in robot swarms. Finally, it discusses reinforcement learning techniques like hierarchical reinforcement learning and decomposition that can help address challenges of modeling large state spaces.
This document discusses engineering autonomic ensembles through model-based development. It describes modeling autonomic systems using Agamemnon and implementing components using Poem. Reinforcement learning is used to find good completions for partial programs that maximize reward. The Service Component Ensemble Language (SCEL) provides an abstract framework for ensemble programming. A case study of a robot ensemble is used to illustrate modeling the domain and requirements, selecting adaptation patterns, modeling behavior, and analyzing requirements through simulation and sensitivity analysis.
The document discusses using swarms of underwater robots to perform search and rescue tasks. It describes the CoCoRo project which uses collective cognition and swarm intelligence to coordinate groups of simple robots. This allows them to display complex emergent behaviors. Specific challenges of operating underwater like communication and localization are addressed. The document proposes using a relay chain to connect an exploratory swarm of robots to a base station. It provides resources to start simulating and developing algorithms for the swarm and relay chain behaviors.
This document discusses a case study on computational self-awareness in smart camera networks. It provides an overview of the EPiCS project, which aims to develop self-aware and self-expressive systems. Surveillance camera networks are presented as an application domain, along with challenges in distributed multi-camera object tracking. The case study then introduces the concept of self-awareness in smart camera networks and provides prerequisites and objectives for participants to develop new strategies for distributed tracking using a simulation environment over the course of a week.
The document discusses how robots may need to be self-aware to be trusted, especially in unpredictable environments. It argues that safety cannot be achieved without self-awareness when a robot's environment is unknown. An internal model allows a robot to simulate possible future actions and outcomes without committing to them. This can provide a minimal level of functional self-awareness for safety. A generic internal modeling architecture is proposed where an internal model evaluates consequences of actions to moderate action selection for safety. Examples of robots using internal models for functions like planning, learning control, and distributed coordination are also provided.
This document discusses the concept of morphogenetic engineering, which aims to design artificial self-organized systems capable of developing elaborate architectures without central planning. It begins by looking at natural complex systems like animal flocking and termite mounds that self-organize. The focus is on "architectures without architects" in biological systems. Morphogenetic engineering is proposed as a new type of engineering that designs self-organizing agents, not the architectures directly, taking inspiration from embryogenesis, simulated development and synthetic biology. Several research projects are summarized that aim to model biological development and create modular, programmable artificial self-construction.
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
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How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
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BÀI TẬP BỔ TRỢ TIẾNG ANH 8 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2023-2024 (CÓ FI...
Introduction to human heuristics by Franco Bagnoli
1. www.aware-project.eu
Introduction to Human Heuristics
Material for social and pervasive computing
Franco Bagnoli & Andrea Guazzini
Center for the Study of Complex Dynamics
University of Firenze, Italy
www.complexworld.net
2. Introduction
Humans do not deal with problems in a “rational” way. They use
“rules of thumb” called heuristics, which are more “economic” than
full rationality, but sometimes fail spectacularly.
Our brain has been selected in a social environment, and we have
developed heuristics to solve social problems, in limited time, with
limited computational capabilities and with limited information
available.
Autonomous agents and portable devices are often confronted with
similar situations, so the adaptation of human decision systems to
computer science might be fruitful.
Moreover, autonomous devices have often to collaborate with
humans, and even act in their delegation.
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 2 / 22
3. Are humans smart?
Humans love to think to be intelligent and to take rational decisions.
Actually, rational thinking is quite slow and computational
demanding. We can discriminate the “usage” of cognitive capabilities
by fMRI and response times. For instance, a good ping-pong player
never “thinks” to the next move.
Some partially “blind” people (blind sight) can detect movements
even if they cannot “understand” what they see.
Human recognition need “emotional” components, otherwise the
subjects cannot even recognise themselves in a mirror.
The signals that initiate a voluntary movement starts about 0.35 s
earlier than the subject’s reported conscious awareness that he/she is
feeling the desire to make a movement. Do we have free will in the
initiation of our movements? Since subjects were able to prevent
intended movement at the last moment, we surely do have a veto
possibility.
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 3 / 22
4. Heuristics as weak intelligence
We have to take a lot of decisions in everyday life.
Generally, these decision are satisfactory, but we all experience
frustration for having chosen the bad choice, or having been cheated.
Twerski and Kahneman examined many situations, and pointed out
the existence of heuristics: “rules of thumb” that are used everyday,
like for instance “prejudicial judgements” based on appearances.
Clearly, if applied to a wrong context, heuristics may fail spectacularly.
Heuristics may be hard-coded (and therefore sometimes called
schemes) or learned.
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 4 / 22
5. Examples of classic heuristics: anchoring
When taking a decision, we rarely “weight” all factors, and generally
rely heavily on just one piece of information (the one easier to recall),
and only in a second moment we “adjust” the answer according to
other factors.
A classical example is the question “Estimate the probability of death
by lung cancer and by vehicle accidents”. People tends to assign a
higher probability to car accidents (since they are much more
commonly reported by press) but lung cancer causes about 3 times
more deaths than cars.
If one asks if Turkey population is more or less than 30 million, and
then asks to estimate that population, the average will be around that
figure (Turkey has about 75 million population).
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 5 / 22
6. Representativeness
People are insensitive to prior probability of outcomes They ignore
preexisting distribution of categories or base rate frequencies. Bayes’
theorem is not easily understood.
People are insensitive to sample size They draw strong inferences
from small number of cases
People have a misconception of chance: gambler’s fallacy. They think
chance will “correct” a series of “rare” events.
People have a misconception of regression. they deny chance as a
factor causing extreme outcome.
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 6 / 22
7. Representativeness examples
Is the roulette sequence “6, 6, 6” more or less probable than “10, 27,
36”?
All kind of stereotypes: black people vs. white people, immigrants,
etc.
There is a murder in New York, and the DNA test (say 99.99%
accuracy both for false positive and false negatives) is positive for the
defendant. There are no other cues. Which is the probability that the
defendant is guilty?
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 7 / 22
8. Heuristics as fast and frugal processing
At present, heuristics have a better reputation: they can be
considered as “optimized” methods of saving computational
resourced and giving faster answers (Gigerenzer).
Many everyday problems would require “unbounded” rationality to be
solved, and a large time for samplig all possibilities.
But we do not try every possible partner when choosing a mate (nor a
tiny fraction of them...).
In a variable world, sometimes the “rules of thumb” are really better
then the weighted methods taught by economists.
In real world, with redundant information, Bayes’ theorem and
“rational” algorithms quickly become mathematically complex and
computationally intractable.
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 8 / 22
9. A new view of heuristics
Ecologically rational (that is, they exploit structures of information in
the environment).
Founded in evolved psychological capacities such as memory and the
perceptual system.
Simple enough to operate effectively when time, knowledge, and
computational might are limited.
Precise enough to be modelled computationally
Powerful enough to model both good and poor reasoning.
(Goldstein & Gigerenzer, 2004)
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 9 / 22
10. Recognition heuristics
In 1991 Gigerenzer and Goldstein asked twelve students in California
and Germany to estimate whether S. Diego or S. Antonio had a larger
population. German students were much more accurate, simply
because most of them did not know S. Antonio.
The same test was performed on soccer outcome, financial estimates,
etc.
But Oppenheim (2003) showed that we use also other cues. If asked
to judge between a known little city and a fictitious one, most of
people would choose the non-existing city.
In any case, there is information in ignorance (and probably
advantages in forgetting).
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 10 / 22
11. Take the best
We often have to choose the “best” (buy a new car).
The most rational thing to do is to maximise a weighted score. The
weights can be extracted by past experiences.
For instance: you are a physicians and have to decide whether a man
with severe chest pain should be sent to the coronary care unit or a
regular nursing bed.
The method based on weighted decision was slow, and had an
efficiency of nearly 50% (i.e., random choice).
A simpler decision tree is much more effective: first consider the most
important factor – had the patient already experienced hart attacks?
If yes, go to intensive unit. Then the second: is the pain localized in
chest? If yes, go to intensive unit, etc. etc.
This is why advertisers focus on “irrelevant” details for selling cars...
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 11 / 22
12. Where do heuristics come from?
Heuristics, like all our brain, is a product of selection.
We are at hand with natural selection, i.e., competition for surviving.
But in order to select a trait in this way, nature has to literally kill
everyone not carrying that trait before reproductive age.
A much less cruel but more effective selection is the sexual one.
In many species, just a tiny fraction of individuals (the leading male,
for instance) do actually reproduce.
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 12 / 22
13. Sexual selection
Sexual selection is so effective, that a tiny improvement in attracting
the opposite sex can result in larger offspring.
This is the origin of the extreme sexual ornaments found in all
sexually-reproducing species.
For humans, the principal ornaments are (probably) power and
dexterity (mainly linguistic): poetry, songs,...
It has been suggested that our “large” brain (with art and all useless
brain products) is just a sexual ornament.
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 13 / 22
14. Machiavellic brain
Monkey and ape societies are often complex social systems.
In such cases, the leading position is conquered by means of alliances,
not by pure muscle power.
This implies large cognitive power, since one needs to elaborate not
only information about others, but also their mutual relationships.
Actually, the size of frontal cortex (the “monkey” brain) correlates
well with the group size (from which one obtains the Dunbar number
for the human group size).
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 14 / 22
15. Logic brain
We find logic problems hard.
How many cards should one turn (at minimum) to check if the following
rule is violated?
Cards with odd digits have a vocal on the back.
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 15 / 22
16. Social brain
But social tasks are easier...
How many cards should one turn (at minimum) to check if the following
rule is violated?
People less than 18 cannot drink alcohol.
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 16 / 22
17. Cooperative brain
We have developed sophisticated methods for eliciting cooperation
and punishing defeaters.
Not surprisingly, this opens the way to (repeated) game theory...
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 17 / 22
18. Example: the ultimatum game
In this game, you are given 10$, and you have to decide how many
dollars you will offer to a third person. He/she can accept and you
share the money, or he/she can refuse and in this case both of you
will loose everything.
How much would you offer?
If you were the third person, up to how much would you accept?
What is the most rational thing to do?
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 18 / 22
19. The dictator game
This is the same as the ultimatum, but in this case the third person
cannot refuse.
How much would you offer in this case?
Before answering, consider the following possibilities:
This third person is sitting near to you.
This third person is somewhere far from you.
You personally know this person and you know that in some future
time he/she can play you present role.
You know that you’ll never meet again this person.
You know that your choice will be made public in your school/office.
What is the most rational thing to do?
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 19 / 22
20. The trust game
This is the same as the dictator, but in this case the third person
initially offers some amount of money, which is doubled by the game
manager. The dictator can decide to give back (partially) or keep for
him/her-self.
How much would you offer initially in this case (third person initial
move)?
Suppose you are offered 5$, which become 10. How much would offer
back if you were the dictator?
What is the most rational thing to do?
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 20 / 22
21. This is the end...
There is a network of nodes that process information coming from
neighbors.
The information can be corrupted, and in this case also the
elaborated information is tainted, like a disease. The node remains
infected only for a limited time.
A node can check the correctness of the received information on a
central repository, but it is costly (say, it takes time).
Try to develop an heuristic for deciding when information should be
checked.
What additional information might be useful for reducing the
infection level while not wasting resources in consultations? The
“trustability” of neighbours? The average level of infection? How
long should the memory last?
How do these solutions depend on the geometry of the network?
What does happen on a regular lattice/disordered graph/scale free
networks?
Franco Bagnoli & Andrea Guazzini (CSDC) Introduction to Human Heuristics 21 / 22