This document analyzes data from the TV game show Goldenballs to study how participants make choices in prisoner's dilemma situations. In the final round, two contestants split or steal a jackpot. Players cooperated (split) 48% of the time, with males cooperating more than females and young players cooperating more than mature players. There was more cooperation between genders than within genders. Players in the same age category cooperated more than those of different ages. Mature players were most efficient at converting jackpots into winnings.
This is a managerial economics presentation on "Game Theory: Prisoners Dilemma" , presented by myself Peerzada Basim. I am a Business student pursuing IMBA degree at University of Kashmir.
I hope this presentation will suffice your need and curiosity of knowing what Game Theory is.
Thank you.
The document discusses the prisoner's dilemma game theory concept where two individuals may choose to cooperate or betray each other, and explains how in the classic prisoner's dilemma scenario, pursuing individual self-interest results in a worse outcome for both rather than cooperation. It provides an example of two prisoners, Dave and Henry, who each must decide whether to plead guilty or not guilty and explores the incentives that lead both to plead guilty even though cooperating by pleading not guilty would result in a shorter total sentence for both of them.
The presentation discusses game theory and strategies for negotiation. It covers topics such as the prisoner's dilemma, Nash equilibriums, dominant strategies, screening techniques, and how perception of rational versus irrational behavior can impact outcomes. Game theory concepts like threats and promises are examined in the context of achieving cooperative outcomes versus outcomes based on rational self-interest.
Game theory is the study of strategic decision making between interdependent parties. The document discusses game theory concepts like pure strategies, payoffs, and the Prisoner's Dilemma. It then applies these concepts to analyze a scenario from the Batman movie where the Joker has rigged two boats to explode unless one boat detonates the other first. The analysis shows how assumptions in the basic Prisoner's Dilemma payoff matrix are oversimplified for this complex real-world scenario.
Prisoner's Dilemma is a paradox in decision analysis in which two individuals acting in their own best interest pursue a course of action that does not result in the ideal outcome. The typical prisoner's dilemma is set up in such a way that both parties choose to protect themselves at the expense of the other participant. As a result of following a purely logical thought process to help oneself, both participants find themselves in a worse state than if they had cooperated with each other in the decision-making process.
In this session, we will be looking at The Prisoner's Dilemma and how it affects our decision making, group and team dynamics, business decisions. We'll look at real world case studies and nature with a goal of understanding this dilemma better.
This document provides tips for negotiating skills and common mistakes. It discusses preparing effectively, understanding other parties, avoiding adversarial approaches, negotiating internally first, and managing emotions. Common mistakes include rigid mindsets, making concessions too early, failing to call timeouts, and not recognizing different negotiating styles. The document presents frameworks for collaborative problem solving and guidelines for competitive negotiations, emphasizing understanding interests, brainstorming options, and managing information.
This is a managerial economics presentation on "Game Theory: Prisoners Dilemma" , presented by myself Peerzada Basim. I am a Business student pursuing IMBA degree at University of Kashmir.
I hope this presentation will suffice your need and curiosity of knowing what Game Theory is.
Thank you.
The document discusses the prisoner's dilemma game theory concept where two individuals may choose to cooperate or betray each other, and explains how in the classic prisoner's dilemma scenario, pursuing individual self-interest results in a worse outcome for both rather than cooperation. It provides an example of two prisoners, Dave and Henry, who each must decide whether to plead guilty or not guilty and explores the incentives that lead both to plead guilty even though cooperating by pleading not guilty would result in a shorter total sentence for both of them.
The presentation discusses game theory and strategies for negotiation. It covers topics such as the prisoner's dilemma, Nash equilibriums, dominant strategies, screening techniques, and how perception of rational versus irrational behavior can impact outcomes. Game theory concepts like threats and promises are examined in the context of achieving cooperative outcomes versus outcomes based on rational self-interest.
Game theory is the study of strategic decision making between interdependent parties. The document discusses game theory concepts like pure strategies, payoffs, and the Prisoner's Dilemma. It then applies these concepts to analyze a scenario from the Batman movie where the Joker has rigged two boats to explode unless one boat detonates the other first. The analysis shows how assumptions in the basic Prisoner's Dilemma payoff matrix are oversimplified for this complex real-world scenario.
Prisoner's Dilemma is a paradox in decision analysis in which two individuals acting in their own best interest pursue a course of action that does not result in the ideal outcome. The typical prisoner's dilemma is set up in such a way that both parties choose to protect themselves at the expense of the other participant. As a result of following a purely logical thought process to help oneself, both participants find themselves in a worse state than if they had cooperated with each other in the decision-making process.
In this session, we will be looking at The Prisoner's Dilemma and how it affects our decision making, group and team dynamics, business decisions. We'll look at real world case studies and nature with a goal of understanding this dilemma better.
This document provides tips for negotiating skills and common mistakes. It discusses preparing effectively, understanding other parties, avoiding adversarial approaches, negotiating internally first, and managing emotions. Common mistakes include rigid mindsets, making concessions too early, failing to call timeouts, and not recognizing different negotiating styles. The document presents frameworks for collaborative problem solving and guidelines for competitive negotiations, emphasizing understanding interests, brainstorming options, and managing information.
Game Theory: An unorthodox take on Prisonerās DilemmaIRJET Journal
Ā
This document discusses the Prisoner's Dilemma game theory concept. It analyzes the classic prisoner's dilemma scenario where two prisoners must decide whether to confess or remain silent. It explains that from a rational self-interest perspective, the dominant strategy for both prisoners is to confess, even though they would both be better off if they cooperated and remained silent. The document also discusses iterative prisoner's dilemmas and how cooperation can emerge through strategies like tit-for-tat. It analyzes how the prisoner's dilemma relates to real-world scenarios involving the tragedy of the commons.
Four teams will participate in a game involving selecting strategies of A or B. The aim is to score the maximum dividends. Scoring is based on the number of As and Bs selected. The document then explains the Prisoner's Dilemma game theory concept where two prisoners can either cooperate or betray each other, and discusses why rational individuals may not cooperate even if it is in their best interest to do so.
This document discusses applying game theory to analyze nuclear proliferation. During the Cold War, the US and Soviet Union were in a prisoner's dilemma, where the dominant strategy for each was to acquire more nuclear weapons. However, stockpiles decreased after peaking in the 1960s. This is because in the long run it became a sequential game, where continuing to increase weapons was not beneficial once an opponent stopped. The equilibrium reached was a Nash equilibrium, where no country could gain by changing strategy as long as others' strategies remained unchanged.
Game theory is the study of strategic decision making where outcomes depend on the choices of multiple players. It originated in the 1920s and was popularized by John von Neumann. Game theory analyzes cooperative and non-cooperative games with various properties like the number of players, information available, and whether choices are simultaneous or sequential. Important concepts in game theory include Nash equilibrium, where no player can benefit by changing strategy alone, and prisoner's dilemma, where defecting dominates but collective cooperation yields higher payoffs. Game theory is now used widely in economics, politics, biology, and other fields involving interdependent actors.
This section addresses some of the social dilemmas that currently affect humanity on a global scale. We will see how game theory has provided tools to study them scientifically, and how cooperation theory is looking for a way out of them.
Cooperation theory research and proposals are grouped into three major areas: strategic, institutional and motivational.
We also review some global dilemmas to understand their inner dynamics, what would have to be done to correct them, and what obstacles there are to achieving this.
Game theory is a strategic decision making study which is used in various studies such as politics, economics, business, biology, etc. Game theory uses mathematical models to describe the outcomes of several choices that a person takes in certain situations, āin the gameā.
The Continuing Constraints on Ireland's Public Financess.coffey
Ā
Ireland still faces constraints on its public finances from deficits and high debt levels, though debt is falling. While expenditure remains high, approximately 80% of corporation tax revenue comes from multinational corporations, highlighting Ireland's vulnerability. To better prepare for economic downturns, fiscal policy should assess balances and debt levels based on GNP rather than GDP, set aside corporate tax revenues equivalent to half the tax rate in a stability fund, and allow withdrawals from this fund during periods of low growth.
This document contains quarterly national accounts data from Ireland for the years 2005-2015. It includes time series charts showing trends in GDP, GNP, consumption, investment, exports, imports and other economic indicators. GDP growth was positive in 2015 Q2 after a long period of recovery from the economic downturn. The data provides a statistical overview of Ireland's economic performance and the contributions of different components to economic growth.
Presentation to Institute of Directors on Q2 economic datas.coffey
Ā
This document analyzes Ireland's recent economic growth and whether it represents a true recovery or statistical anomaly. It provides statistics showing that Ireland's GDP and GNP have grown significantly in recent years, with GDP growth reaching 7.7% in 2014, the fastest pace since 2007. This growth has been driven by a rebound in domestic demand and a strong increase in exports. Economists and media outlets have reacted positively but some question if the strength of the recovery can be sustained.
The document contains quarterly national accounts data from Ireland for 2005-2014. It includes time series charts and tables showing trends in key macroeconomic indicators such as GDP, GNP, consumption, investment, exports, imports and economic growth rates. Overall GDP growth was positive in 2014 Q2 according to the seasonally adjusted constant price data.
The document discusses the Six-Pack, Two-Pack and Fiscal Compact which introduced stricter fiscal rules for EU countries. It outlines the key provisions including medium-term budgetary objectives of -1% to balance of GDP, annual improvements of 0.5% of GDP to structural balance, and reducing excess debt by 1/20th annually. It also examines macroeconomic indicators for Ireland like growth, deficits, debt levels, and concludes that increased monitoring and enforcement aims to prevent future crises but may not solve the ongoing crisis.
The document discusses Ireland's growing public debt crisis. It estimates that Ireland's general government debt will reach approximately ā¬250 billion by 2014, up drastically from ā¬47 billion in 2007. This growth is primarily due to large budget deficits from 2008-2011, billions borrowed to recapitalize banks, and promissory notes issued to distressed financial institutions. While some assets may offset this debt, sustainability concerns remain due to risks of further bank losses, deficit overruns, and debt interest costs totaling billions annually. The outlook remains uncertain depending on maintaining deficit reduction and economic recovery.
The document discusses the key fiscal rules and targets contained in the proposed Fiscal Stability Treaty, including:
- Limiting annual deficits to 3% of GDP and requiring debt levels not exceed 60% of GDP (Maastricht criteria)
- Requiring structural deficits be reduced by between 0.75-1.5% of GDP annually if above limits (Public Finances Correction Rule)
- Limiting expenditure growth to potential GDP growth in strong economic times (Sustainable Expenditure Growth Rule)
- Additional measures like macroeconomic imbalance scorecard and linking access to ESM funds on ratifying the treaty.
The document contains graphs and data on key Irish economic indicators such as GDP, components of GDP, trade balances, imports and exports from 2005-2011. It shows that after steady growth, GDP declined in 2008-2009 due to the financial crisis but resumed growing in 2010-2011. Exports initially drove growth but were later balanced by increasing domestic demand.
1) Professor Morgan Kelly warned in several Irish Times articles between 2006-2011 that Ireland's public debt crisis could result in national bankruptcy as property prices collapsed, unemployment rose, and bank losses mounted.
2) Kelly estimated that Ireland's total government debt could reach ā¬250 billion by 2014 due to annual deficits, bank bailouts, and promissory note interest. Others argued Kelly's estimate was too high by ā¬50-60 billion.
3) Breaking down potential debt sources, the author estimates Ireland's government debt could realistically reach ā¬210 billion by 2014, with bank-related debt accounting for 25% of the total and annual deficits contributing ā¬100 billion overall.
The National Accounts were updated for Q4 2010. GDP growth was revised upwards to show an increase of 0.5% over the previous quarter, rather than the preliminary estimate of 0.3%. Consumer spending and business investment were stronger than initially estimated, contributing to the upward revision in GDP growth in Q4 2010. Inflation remained subdued, with CPI inflation of 3.3% year-on-year in December 2010.
The Irish Economy Going Into 2011: Prospects of an Export Led Recoverys.coffey
Ā
The Irish economy is turning the corner towards recovery in 2011 according to the Minister's statement on the 2010 Exchequer Returns. The public finances have stabilized and economic data from the third quarter of 2010 supports achieving the targets in Budget 2011. Export-led growth has been the government's strategy and it is working, with exports reaching an all-time high in 2010 and growing 6.2% over 2009, led by strong performances in manufacturing and agri-food. While the state continues to borrow more than sustainable, following the National Recovery Plan provides real grounds for optimism about the Irish economy entering 2011.
Game Theory: An unorthodox take on Prisonerās DilemmaIRJET Journal
Ā
This document discusses the Prisoner's Dilemma game theory concept. It analyzes the classic prisoner's dilemma scenario where two prisoners must decide whether to confess or remain silent. It explains that from a rational self-interest perspective, the dominant strategy for both prisoners is to confess, even though they would both be better off if they cooperated and remained silent. The document also discusses iterative prisoner's dilemmas and how cooperation can emerge through strategies like tit-for-tat. It analyzes how the prisoner's dilemma relates to real-world scenarios involving the tragedy of the commons.
Four teams will participate in a game involving selecting strategies of A or B. The aim is to score the maximum dividends. Scoring is based on the number of As and Bs selected. The document then explains the Prisoner's Dilemma game theory concept where two prisoners can either cooperate or betray each other, and discusses why rational individuals may not cooperate even if it is in their best interest to do so.
This document discusses applying game theory to analyze nuclear proliferation. During the Cold War, the US and Soviet Union were in a prisoner's dilemma, where the dominant strategy for each was to acquire more nuclear weapons. However, stockpiles decreased after peaking in the 1960s. This is because in the long run it became a sequential game, where continuing to increase weapons was not beneficial once an opponent stopped. The equilibrium reached was a Nash equilibrium, where no country could gain by changing strategy as long as others' strategies remained unchanged.
Game theory is the study of strategic decision making where outcomes depend on the choices of multiple players. It originated in the 1920s and was popularized by John von Neumann. Game theory analyzes cooperative and non-cooperative games with various properties like the number of players, information available, and whether choices are simultaneous or sequential. Important concepts in game theory include Nash equilibrium, where no player can benefit by changing strategy alone, and prisoner's dilemma, where defecting dominates but collective cooperation yields higher payoffs. Game theory is now used widely in economics, politics, biology, and other fields involving interdependent actors.
This section addresses some of the social dilemmas that currently affect humanity on a global scale. We will see how game theory has provided tools to study them scientifically, and how cooperation theory is looking for a way out of them.
Cooperation theory research and proposals are grouped into three major areas: strategic, institutional and motivational.
We also review some global dilemmas to understand their inner dynamics, what would have to be done to correct them, and what obstacles there are to achieving this.
Game theory is a strategic decision making study which is used in various studies such as politics, economics, business, biology, etc. Game theory uses mathematical models to describe the outcomes of several choices that a person takes in certain situations, āin the gameā.
The Continuing Constraints on Ireland's Public Financess.coffey
Ā
Ireland still faces constraints on its public finances from deficits and high debt levels, though debt is falling. While expenditure remains high, approximately 80% of corporation tax revenue comes from multinational corporations, highlighting Ireland's vulnerability. To better prepare for economic downturns, fiscal policy should assess balances and debt levels based on GNP rather than GDP, set aside corporate tax revenues equivalent to half the tax rate in a stability fund, and allow withdrawals from this fund during periods of low growth.
This document contains quarterly national accounts data from Ireland for the years 2005-2015. It includes time series charts showing trends in GDP, GNP, consumption, investment, exports, imports and other economic indicators. GDP growth was positive in 2015 Q2 after a long period of recovery from the economic downturn. The data provides a statistical overview of Ireland's economic performance and the contributions of different components to economic growth.
Presentation to Institute of Directors on Q2 economic datas.coffey
Ā
This document analyzes Ireland's recent economic growth and whether it represents a true recovery or statistical anomaly. It provides statistics showing that Ireland's GDP and GNP have grown significantly in recent years, with GDP growth reaching 7.7% in 2014, the fastest pace since 2007. This growth has been driven by a rebound in domestic demand and a strong increase in exports. Economists and media outlets have reacted positively but some question if the strength of the recovery can be sustained.
The document contains quarterly national accounts data from Ireland for 2005-2014. It includes time series charts and tables showing trends in key macroeconomic indicators such as GDP, GNP, consumption, investment, exports, imports and economic growth rates. Overall GDP growth was positive in 2014 Q2 according to the seasonally adjusted constant price data.
The document discusses the Six-Pack, Two-Pack and Fiscal Compact which introduced stricter fiscal rules for EU countries. It outlines the key provisions including medium-term budgetary objectives of -1% to balance of GDP, annual improvements of 0.5% of GDP to structural balance, and reducing excess debt by 1/20th annually. It also examines macroeconomic indicators for Ireland like growth, deficits, debt levels, and concludes that increased monitoring and enforcement aims to prevent future crises but may not solve the ongoing crisis.
The document discusses Ireland's growing public debt crisis. It estimates that Ireland's general government debt will reach approximately ā¬250 billion by 2014, up drastically from ā¬47 billion in 2007. This growth is primarily due to large budget deficits from 2008-2011, billions borrowed to recapitalize banks, and promissory notes issued to distressed financial institutions. While some assets may offset this debt, sustainability concerns remain due to risks of further bank losses, deficit overruns, and debt interest costs totaling billions annually. The outlook remains uncertain depending on maintaining deficit reduction and economic recovery.
The document discusses the key fiscal rules and targets contained in the proposed Fiscal Stability Treaty, including:
- Limiting annual deficits to 3% of GDP and requiring debt levels not exceed 60% of GDP (Maastricht criteria)
- Requiring structural deficits be reduced by between 0.75-1.5% of GDP annually if above limits (Public Finances Correction Rule)
- Limiting expenditure growth to potential GDP growth in strong economic times (Sustainable Expenditure Growth Rule)
- Additional measures like macroeconomic imbalance scorecard and linking access to ESM funds on ratifying the treaty.
The document contains graphs and data on key Irish economic indicators such as GDP, components of GDP, trade balances, imports and exports from 2005-2011. It shows that after steady growth, GDP declined in 2008-2009 due to the financial crisis but resumed growing in 2010-2011. Exports initially drove growth but were later balanced by increasing domestic demand.
1) Professor Morgan Kelly warned in several Irish Times articles between 2006-2011 that Ireland's public debt crisis could result in national bankruptcy as property prices collapsed, unemployment rose, and bank losses mounted.
2) Kelly estimated that Ireland's total government debt could reach ā¬250 billion by 2014 due to annual deficits, bank bailouts, and promissory note interest. Others argued Kelly's estimate was too high by ā¬50-60 billion.
3) Breaking down potential debt sources, the author estimates Ireland's government debt could realistically reach ā¬210 billion by 2014, with bank-related debt accounting for 25% of the total and annual deficits contributing ā¬100 billion overall.
The National Accounts were updated for Q4 2010. GDP growth was revised upwards to show an increase of 0.5% over the previous quarter, rather than the preliminary estimate of 0.3%. Consumer spending and business investment were stronger than initially estimated, contributing to the upward revision in GDP growth in Q4 2010. Inflation remained subdued, with CPI inflation of 3.3% year-on-year in December 2010.
The Irish Economy Going Into 2011: Prospects of an Export Led Recoverys.coffey
Ā
The Irish economy is turning the corner towards recovery in 2011 according to the Minister's statement on the 2010 Exchequer Returns. The public finances have stabilized and economic data from the third quarter of 2010 supports achieving the targets in Budget 2011. Export-led growth has been the government's strategy and it is working, with exports reaching an all-time high in 2010 and growing 6.2% over 2009, led by strong performances in manufacturing and agri-food. While the state continues to borrow more than sustainable, following the National Recovery Plan provides real grounds for optimism about the Irish economy entering 2011.
The document discusses budget 2011 and government expenditure from the perspective of Seamus Coffey in the Department of Economics. It focuses on expenditure savings for central government spending. However, it notes that there is one little problem with the expenditure savings plans.
The government spends money on many programs and services each year. In 2009, the largest areas of central government spending were on healthcare at 24% of the budget, education at 15%, and social security benefits at 14%. Defense spending accounted for another 12% while interest on the national debt made up 7% of central government expenditures.
The document discusses how Ireland's budget deficit projection increased from ā¬3 billion to ā¬6 billion between 2009 and 2010. This ā¬3 billion increase was due to several factors: ā¬1 billion from reductions to nominal GDP projections, ā¬1 billion from the continued deterioration of public finances, and ā¬1 billion from lowering the target for the general government balance (GGB) from 10% to 9.3% of GDP. Reduced growth forecasts did not contribute to the increased deficit projection.
The document discusses quarterly GDP and GNP statistics from 2005 and 1997. It contains information on gross domestic product and gross national product for multiple quarters and years.
GDP and GNP figures for 2005 are presented on a quarterly basis. The document also contains quarterly GDP and GNP data from 1997. Overall, the document provides economic performance data from two different years broken down by quarter.
The document analyzes Ireland's economic performance in 2010. It shows that while consumption and investment continued declining in the first three quarters of 2009, exports remained relatively stable. The sectors hit hardest by the recession were building/construction and agriculture. The largest export markets were the US, Belgium, and UK, though exports declined to most countries. NAMA is expected to pay ā¬54 billion to acquire ā¬77 billion in bank loans now valued at ā¬47 billion, representing a 30% haircut. AIB estimates it will transfer ā¬23 billion in loans to NAMA and receive ā¬19 billion in return, an 18% discount.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
Ā
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
Ā
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
Ā
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Ā
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
Ā
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Ā
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
Ā
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
Ā
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Ā
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Ā
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
Ā
An English š¬š§ translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech šØšæ version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Ā
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Ā
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Ā
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Ā
A Natural Experiment in the Prisoner's Dilemma
1. DEPARTMENT OF ECONOMICS
UNIVERSITY COLLEGE CORK
WORKING PAPER SERIES
SPLIT OR STEAL? A NATURAL EXPERIMENT OF THE
PRISONERāS DILEMMA.
Working Paper: 09-XX*
Seamus Coffey
Health Economics Group
Department of Economics
University College Cork
ABSTRACT: This paper uses the final round of the UK TV game Goldenballs as a natural
experiment to analyse the choices made by people when faced with a prisonerās dilemma type
situation. In the game two contestants make a āsplitā or āstealā to decide how a jackpot of
varying size is to be distributed ā split, stolen or lost. Players cooperate 48% of the time with
males cooperating more than females and young players cooperating more than mature
players. There is considerably more cooperation in games between genders than in games
with players of the same gender. Players in the same age category cooperate more with each
other than players in different age categories. Mature players are the most efficient players at
converting jackpots into winnings.
JEL Classification Numbers: C72, C93, D64
Keywords: Prisonerās Dilemma, Natural Experiment, Cooperation, Gender Differences, Age
Differences.
Correspondence:
Address: Department of Economics, University College Cork, Cork City, Ireland.
Email: s.coffey@ucc.ie
Telephone: 353 21 4901928
Fax: 353 21 4273920
* This working paper represents a work in progress, circulated to encourage discussion and comments, and should be read as
such. This work should not be quoted without permission from the author. Any opinions expressed in this work are those of
the author and do not necessarily reflect the views of the Department of Economics, University College Cork.
2. 1. Introduction
The TV game show Goldenballs concludes with two contestants facing off in a situation that
is a variation of classic set-up of The Prisonerās Dilemma. The Prisonerās Dilemma is the
most frequently used example in analysing situations where people will benefit from co-
operating but have an individual incentive for non-cooperation. Using data from the show
this paper considers the characteristics of people who choose to cooperate, and the impact, if
any, that the characteristics of their opponent have.
Overall, players cooperate 48% of the time with males cooperating more than females and
young players cooperating more than mature players. There is considerably more
cooperation in games between genders than in games with players of the same gender.
Players in the same age category cooperate more with each other than players in different age
categories. Mature players are the most efficient players at converting jackpots into
winnings.
2. The Prisonerās Dilemma
Following Ryan and Coffey (2006) the game is generally described using the following
analogy:
Two prisonerās have been arrested under the suspicion of having committed murder and are
placed in separate isolation cells. Both care much more about their personal freedom than
about the welfare of their accomplice. The police have insufficient evidence for a conviction
and offer each of the prisoners the same deal: 1
āYou may choose to confess or remain silent. If you confess and your accomplice
remains silent I will drop all charges against you and use your testimony to ensure
that your accomplice receives the full 25-year sentence. Likewise, if your accomplice
confesses while you remain silent, they will go free while you do the time. If you both
confess I get two convictions, but I'll see to it that you both get early releases after ten
years. If you both remain silent, I'll have to settle for 4 year sentence on firearms
possession charges.ā
1
This situation is set up and described as a Prisonerās Dilemma in the 2002 film Murder by Numbers when two
suspects are arrested and questioned on suspicion of murder in the manner described here.
2
3. Each prisoner must make the choice of whether to remain silent (co-operate with his
accomplice) or confess (defect and betray his accomplice). A one-shot, two-player prisonersā
dilemma can be summarized as follows:
Table 1: Payoff matrix for the classic prisonerās dilemma
Prisoner 2
Confess Stay Silent
Confess 10yrs, 10yrs free, 25yrs
Prisoner 1
Stay Silent 25yrs, free 4yrs, 4yrs
The dilemma arises when one assumes that both prisoners only care about minimising their
own jail terms, i.e. that is they are seeking to minimise the numbers in the above pay-off
matrix. In each cell the first prison sentence listed corresponds to the row player, Prisoner 1,
and the second prison sentence corresponds to the payoff for the column player, Prisoner 2.
We can see that the outcome of each choice for a prisoner depends on the choice of the
accomplice.
The problem with the Prisonersā Dilemma is that if both decision-makers were purely
rational, they would never cooperate. If Prisoner 1 assumes that Prisoner 2 will confess he
should also confess, giving a 10 year sentence rather than the 25 years for remaining silent.
If he assumes that Prisoner 2 will remain silent his best course of action is also to confess as
this will mean no jail time versus four years for remaining silent. Thus, we see that for
Prisoner 1 non-co-operation with his accomplice or confessing is his dominant strategy. A
similar analysis for Prisoner 2 will show that confess of also a dominant strategy for him.
Thus the Nash Equilibrium for this game is for both prisoners to confess and each receives a
jail sentence of ten years. It is easy to see that this is not the best collective outcome for the
prisoners.
If reasoned from the perspective of the optimal outcome the correct choice would be for the
prisoners to cooperate with each other and deny the allegations, as this would reduce the total
jail time served. Any other decision would be worse for the two prisoners considered
together. When the prisoners both confess, each prisoner achieves a worse outcome than if
3
4. they had both denied. This demonstrates that in a non-zero sum game the Pareto optimum and
the Nash equilibrium can be opposite.
The only way to achieve the Pareto optimal solution in the one-shot Prisonersā Dilemma is if
a prior agreement to deny is somehow enforceable. This would clearly be in the prisonerās
joint interests. Unless the agreement to deny is enforced in some way the incentive for both
prisoners to confess is so strong that neither can trust the other to keep to any agreement. If
Prisoner 1 sticks to the agreement, Prisoner 2 can go free by defecting on the agreement and
confessing.
A significant amount of research on the Prisonersā Dilemma relates to evidence of collusion
and cooperative behaviour. This type of behaviour contradicts the theoretical prediction that
non-co-operation is the dominant strategy. For example, large firms can and do collude. In
an experimental setting Camerer (2003) points out that people playing one-shot Prisonersā
Dilemma games cooperate around fifty percent of the time.
3. The Goldenballs Dilemma
Several researchers have used television game shows provide a natural venue to observe real
decisions in an environment with high stakes. For example, in the U.S., Berk, Hughson, and
Vandezande (1996) study contestantsā behaviour on The Price is Right to investigate rational
decision theory, Gertner (1993) and Beetsma and Schotman (2001) make use of data from
Card Sharks and Lingo, respectively, to examine individual risk preferences and, finally,
Metrick (1995) uses data from Jeopardy! to analyse behaviour under uncertainty and playersā
ability to choose strategic best responses.
The example chosen here is from series one of the UK game show Goldenballs. The dataset
comprises the entire 40 episodes broadcast between June and August 2007. All 40 episodes
were recorded before the show began screening. It is the final element of the game āsplit or
stealā that is our primary focus but what follows is a brief description of how the final two
players are chosen and the amount of the jackpot they will be playing for.
Round 1: Each show begins with four players, two male and two female and a drum
containing 100 āgolden ballsā with cash values ranging from Ā£10 to Ā£75,000. 12 balls are
drawn at random from this drum and these along with four ākillerā balls are distributed
4
5. between the four contestants.2 Each contestant has four balls. The contents (cash value or
ākillerā) of two are visible to all players, while the contents of the remaining two balls are
visible only to their owner.
In turn, the contestants announce the contents of their hidden golden balls. They can either
tell the truth or lie about their amounts. After each contestant has done this, they discuss who
they think is lying and try to establish who has the worst set of golden balls, either in terms of
having the lowest amount of money or the most ākillerā balls.
The contestants then secretly vote for which of them they would like to leave the game and
the player who receives the most votes is eliminated. At the end of the round, each contestant
reveals the contents of the golden balls on their back row and the eliminated contestant's
golden balls are "binned", and are out of the game for good.
Round 2: The three remaining contestants' golden balls are put back into the drum, along
with two more cash balls, as well as one more ākillerā ball, leaving fifteen golden balls in
play. These fifteen golden balls are split among the remaining three contestants randomly.
Again the contents of two of the balls are visible to all players with the contents of the
remaining three hidden. The game proceeds are per Round 1 with a secret vote determining
the player to be eliminated.
Bin or Win: The remaining ten balls plus one additional ākillerā are placed on a table balls.
The players take it turn to select a ball to "bin" (eliminate from the game) and a ball to "win"
(add to the jackpot). Cash values are added to the jackpot. If a ākillerā ball is picked to be
won, then the accumulative value of the jackpot is divided by 10. This process is repeated
five times.
Split or Steal: It is at this stage that the contestants face a decision similar to the Prisonerās
Dilemma as they have to make a decision about the final jackpot. Each contestant chooses a
ball, either āsplitā, which means they try and split the jackpot with the other contestant or
āstealā which means they try and steal the entire jackpot for themselves. There are three
outcomes as follows:
2
At the end of the game if a ākillerā ball remains and is chosen as one of the five balls that will make up the
value of the jackpot, the ākillerā ball will result in the jackpot being ten times smaller.
5
6. ļ· Both players choose āsplitā: The winnings are split equally between them.
ļ· One player chooses āstealā, one āsplitā: The player who played āstealā gets all the
money.
ļ· Both players choose āstealā: No-one gets any money.
The problem is the same as The Prisonerās Dilemma except it is not quite as pure. This is a
one shot game, but the players are in the same room, in fact, theyāre looking right at each
other, their friends and family are watching and they are given the opportunity to convince
the other person of their intention to either āsplitā or āstealā. There is more at stake than some
money, their reputation amongst all people for one. On top of all of this they have been
playing a game for the past half hour and have had the chance to betray each other already.
The similarities with the Prisoner's Dilemma are:
1. It is a game of cooperation (split) or defection (steal).
2. Decisions are made simultaneously.
3. It is a one shot game
The major differences are:
1. This is a zero-sum game.
2. The players can communicate.
3. Steal (defect) is only a weakly dominant strategy
Each player has an incentive to defect and play āstealā because he is never worse off
monetarily for doing so. Table 2 is a payoff matrix for the game.
Table 2: Payoff matrix for the Goldenballs āSplitā or āStealā round
Player 2
Steal Split
Steal 0%, 0% 100%, 0%
Player 1
Split 0%, 100% 50%, 50%
The worst outcome in this game is for the players to both choose āstealā as that would mean
no one wins the jackpot. All other scenarios mean the full jackpot is given to at least one of
the players. At initial inspection it may appear that the jackpot will be given out Ā¾ times and
no jackpot a Ā¼ of the time. But the interesting thing with this game is that assuming all
6
7. players behave rationally the outcome will actually always be that no one wins the jackpot
(i.e. two steals).
If you put yourself in the position as a player, you can see how this works. There are two
possible options that your opponent can choose (āstealā or āsplitā).
Take scenario 1 where your opponent chooses āsplitā. Here if you choose āsplitā you will get
half the jackpot, if you choose āstealā you will get the entire jackpot. So obviously, any
rational person will choose āstealā as this will maximise their winnings.
Take scenario 2 where your opponent chooses āstealā, in this scenario it is irrelevant whether
you choose āstealā or āsplitā because either way you will get nothing. So given the scenario 2
decision is irrelevant (as āstealā and āsplitā both result in 0) your decision should be based
purely on scenario 1 where it has already been illustrated that any rational person will choose
āstealā.
So the optimum strategy for any player is āstealā. Of course the problem with this is that your
opponent has the same options as you and therefore will pick āstealā which means the game
ends in two āstealsā. So going back to the game show assuming that all participants are
rational human beings the first 55 minutes of the show are irrelevant because whatever the
jackpot ends up being the result of the game will always end up with no one wining anything.
So what actually happens when people are faced with this choice on the show? The show is
currently half way through its sixth series and, in the five and a half series to date, 253
episodes have been broadcast. The paper uses data on the 40 episodes in series one that were
broadcast in 2007. This gives us a sample of 80 people who were presented with the
Goldenballs Dilemma.
List (2006) provides a number of useful caveats when considering data from a game show
setting. First, those who appear on the show may not be drawn randomly from the population
of interest. Second, the public nature of the play may affect behaviour so that people do not
consider simply a one-off game with the other contestant but as part of a repeated game with
those who view the show.
7
8. 4. The Data
Summary statistics of the 80 participants in the sample are provided in table 3. This provides
an overview of the amounts earned in the first three rounds, i.e. the jackpot played for, the
cooperation rates and the average amount of money won. The final column is a measure of
the participantsā ability to transform jackpots into winnings, the efficiency rate.3
Even though we have shown that 'steal' is the weakly dominant strategy of the 80 contestants,
42 of them chose 'split', or just over 52%, with the other 38 contestants obviously choosing
'steal'. This is in line with previous findings of cooperation rates in other trials and
experiments of the prisonerās dilemma.
Table 3: Summary of participantsā characteristics, choices and outcomes
N % Average Cooperation Average Average
Jackpot Rate Winnings Winnings /
(Std. Dev.) (Std. Dev.) Ā½ Average
Jackpot
Overall 80 - Ā£12,976 0.52 Ā£5,395 0.83
(15,992) (11,511)
Male 37 46% Ā£9,192 0.46 Ā£4,320 0.94
(12,990) (9,555)
Female 43 54% Ā£16,231 0.58 Ā£6,320 0.78
(17,690) (13,004)
White 76 95% Ā£12,944 0.51 Ā£5,333 0.82
(16,014) (11,667)
Non-White 4 5% Ā£13,587 0.75 Ā£6568 0.97
(17,952) (9181)
Young 37 46% Ā£11,480 0.49 Ā£2,469 0.43
(14,990) (5,845)
Mature 43 54% Ā£14,262 0.56 Ā£7,912 1.11
(16,874) (14,350)
Of the 43 females who made it to the final round, 24 (or 58%) chose āsplitā, while of the 37
males only 17 (or 46%) chose āsplitā. Female had higher average winnings than males, but
3
This figure will lie between zero and two. A figure of one would mean that on average each member of this
group won half of the available jackpot. A figure of less than one indicates that the average winning was less
than half of the average jackpot meaning that some jackpots were lost or stolen on this group. A figure of
greater than one means that this group won more than half the jackpot on average, meaning there were some
successful stealers in this group and relatively fewer suckers who had jackpots stolen on them. A figure of two
would mean that all members this group successfully stole the jackpots they played for. If there are games
between members of the same group the maximum efficiency figure will be less than two.
8
9. this is primarily because they played for bigger jackpots. If we look at efficiency rates males
have a rate of 0.94, while for females the figure is only 0.78.
The only group who had an efficiency rate of greater than one, that is their average winnings
were greater than half the average jackpot played for, were the mature group with an
efficiency rate of 1.11. In contrast the young participants had the worst efficiency rate of
only 0.43. On average they won less than a quarter of the total jackpot amounts they played
for.
The average jackpot competed for in the 40 episodes was Ā£12,975.76, ranging from just Ā£3 to
Ā£61,060. Table 4 gives further details on the jackpots and the actual outcomes of the 40
games played.
Table 4: Summary of jackpots played for
Outcome N % Average Standard Minimum Median Maximum
Jackpot Deviation
All Games 40 - Ā£12,976 15,992 Ā£3 Ā£7,108 Ā£61,060
Lost 10 25% Ā£8,742 14,695 Ā£455 Ā£3,815 Ā£50,450
Stolen 18 45% Ā£17,807 18,308 Ā£3 Ā£13,265 Ā£61,060
Split 12 30% Ā£9,245 111,06 Ā£32 Ā£5,109 Ā£38,950
There were 12 episodes in which both contestants chose 'split' and the jackpot was divided
between them. The average split jackpot was Ā£9,245.49. That leaves 18 people choosing
'split' who had 'steal' played against them and ended up with nothing. The average stolen
jackpot was Ā£17,807.14. In the remaining ten episodes both contestants choose 'steal' and the
jackpot was lost. The average lost jackpot was Ā£8,742.25.
Across the 40 games a total prize fund of Ā£519,030.50 was played for. The 10 ālostā jackpots
came to a total of Ā£87,422.50. This means our 80 contestants had an efficiency rate of 0.83.
17% of the total available winnings were lost due to non-cooperation by both participants.
If strategies were played randomly we would expect the jackpot to be split 25% of the time,
stolen 50% of the time and lost 25% of the time. The actual percentages of 30%, 45% and
25% only differ ever slightly from this with slightly more splits than steals as predicted by
purely random behaviour.
9
10. 5. Decision Factors
We will now consider a number of factors that may have an impact on the decisions the
players make in the āsplitā or āstealā round. The factors considered include, the size of the
jackpot, gender and gender of opponent, age and age of opponent, profession and hair colour.
Size of Jackpot: In games with the 12 biggest jackpots (Ā£61,060 to Ā£16,600, average
Ā£32,968.33) āsplitā is played 13 times. In games with the 12 smallest jackpots (Ā£3 to Ā£1,815,
average Ā£755.58) āsplitā is played is played 12 times. This is 54% and 50% of the time in
each case. This suggests that the size of jackpot is not a significant determinant of the
strategy played. If we look at the outcomes of the 12 biggest jackpots, 9 are successfully
stolen (75%), with 2 split and 1 lost. Of the 12 smallest jackpots only 4 are successfully
stolen (33%) with 4 split and 4 lost.
Gender Differences: Of the 40 games, 23 were male versus female, 7 were male versus
male and 10 were female versus female. These are summarised in Table 5.
Table 5: Outcomes of games by gender
Male Female
Number = 7 Number = 23
Lost = 3; Stolen = 1; Split = 3 Lost = 6; Stolen = 8; Split = 9
Average Jackpot = Ā£3,478 Average Jackpot = Ā£12,670
Lost = Ā£2,935; Stolen = Ā£648; Split = Ā£13,600 Lost = Ā£11,125; Stolen= Ā£17,377; Split = Ā£9,516
Male Cooperation Rate = 0.35 Cooperation Rate = 0.57
Average Winnings = Ā£1,110 Male = 0.52; Female = 0.61
Efficiency Rate = 0.64 Average Winnings = Ā£4,884
Male = Ā£6,275; Female = Ā£3,494
Efficiency Rate = 0.77
Male = 0.99; Female = 0.55
Number = 10
Lost = 1; Stolen = 7, Split = 2
Average Jackpot = Ā£20,327
Lost = Ā£11,872 ; Stolen = Ā£25,653 ; Split = Ā£5,916
Female
Cooperation Rate = 0.55
Average Winnings = Ā£9,570
Efficiency Rate = 0.94
10
11. Each quadrant represents the different types of game (male versus male, male versus female,
female versus female) as indicated by the row and column markers. The number of each type
of game is given as well as the breakdown of split, stolen or lost outcomes of these games.
The average jackpot played for, the co-operation rate of the participants, the average winning
and the efficiency rate for each type of game is given. Additional data by gender is given for
male versus female games.
Against females, females played āsplitā 55% of the time and played it 61% of the time against
males. Males played āsplitā 52% of the time against females but only 35% of the time against
males. There is noticeably more cooperation across genders than amongst genders.
Of the 12 games where the jackpot was split, 9 were in games where there was a male and a
female (40% of male versus female games), while only 1 was in an all male game (14% of all
male games) and 2 were in all female games (20% of all female games). In the 17 games of
the same gender the jackpot was split only 3 times (18% of same gender games).
70% of female versus female games resulted in a successful āstealā! With only 10% of
jackpots lost, female versus female games were the most efficient, though clearly not the
most equitable. The amount lost was only 6% in all female games, but this is largely due to
the high rate of successful steals. 43% of male versus male games ended in a successful
steal, but with 43% of jackpots also lost the efficiency rate of male versus male games was
only 0.64. Of the 8 steals in the male versus female games (34% of such games), 5 were by
males and 3 by females. The overall efficiency rate in male versus female games was 0.77,
but males fared substantially better with a rate of 0.99 against 0.55 for females.
Age Differences: The players were broken into two age categories. āYoungā are those
players who are less than 30. āMatureā are players above 30. 37 players are the young
category with 43 in the mature category. There were 11 games between two young
contestants, 14 games between two mature contestants and 15 of the games featured a young
player against a mature player. The breakdown of these games by age category is in table 6.
Against young opponents, young players played āsplitā 55% of the time and played it 40% of
the time against mature opponents. Mature players played āsplitā 75% of the time against
other mature players but only 20% of the time against young players. There is noticeably
11
12. more cooperation amongst the age categories than between them, particularly in the mature
age category.
Table 6: Outcome of games by age
Young Mature
Number = 11 Number = 15
Lost = 4; Stolen = 2; Split = 5 Lost = 6; Stolen = 9; Split = 0
Average Jackpot = Ā£10,113 Average Jackpot = Ā£13,487
Lost = Ā£17,320; Stolen = Ā£7,105; Split = Ā£5,551 Lost = Ā£3,024; Stolen = Ā£20,462; Split = n/a
Young Cooperation Rate = 0.55 Cooperation Rate = 0.30
Average Winnings = Ā£1,907 Young = 0.40; Mature = 0.20
Efficiency Rate = 0.37 Average Winnings = Ā£6,138
Young = Ā£3,293; Mature = Ā£8,984
Efficiency Rate = 0.91
Young = 0.48; Mature = 1.33
Number = 14
Lost = 0; Stolen = 7; Split = 7
Average Jackpot = Ā£14,677
Mature Lost = n/a; Stolen = Ā£17,451; Split = Ā£11,904
Cooperation Rate = 0.75
Average Winnings = Ā£7,339
Efficiency Rate = 1.00
None of the 15 games between a young player and a mature player resulted in a split pot.
Young players split 5 of their 11 games (45%) and mature players split 7 of their 14 games
(50%).
The efficiency rate of young players is very low. In games amongst themselves young
players only make to convert 37% of the jackpot amounts available into winnings. They lost
4 of the 11 jackpots they played for with the average lost jackpot equal to Ā£17,320. Young
players fared slightly better in games versus mature players but the efficiency rate was still
less than 0.50.
The average efficiency rate of young versus mature games was high with only 9% of the
money lost. However, mature players were the main winners with an efficiency rate of 1.33.
In the 9 young versus mature games where there was a successful steal, six of the steals were
carried out by mature contestants and three by young contestants. The six mature contestants
stole an average of Ā£22,460 off young contestants. By comparison, the three young
12
14. Table 8: Professions of players involved in stolen jackpots (gender in brackets)
Stealers Suckers Jackpot
Marketing Manager (F) Learning Support Worker (M) Ā£6,500
Area Manager (F) Storyteller (F) Ā£47,250
IT Manager (F) Marketing Consultant (M) Ā£7,710
Singer (M) Civil Servant (M) Ā£3
Sales Assistant (F) Trainee Accountant (F) Ā£20,220
Emergency Call Operator (F) Drama Tutor (M) Ā£23,315
Train Conductor (M) Actor (M) Ā£126
Car Dealer (M) Civil Servant (F) Ā£50,500
Chef (M) Police Officer (F) Ā£19,560
Student (M) Collection Agent (M) Ā£1,815
Nurse (F) Housewife (F) Ā£4,188
Teacher (M) Marketing Assistant (F) Ā£16,600
Company Director (F) Advertising Executive (F) Ā£66
Roofer (M) Hypnotherapist (F) Ā£9,930
Recruitment Consultant (M) Rtd. Post Mistress (F) Ā£17,400
Business Analyst (F) Project Co-ordinator (F) Ā£4,100
Social Events Organiser (F) Account Executive (F) Ā£61,060
Mortgage Broker (F) Office Manager (F) Ā£30,185
6. Conclusion
The final part of the Goldenballs game show provides a natural experiment of a high stakes
prisonerās dilemma. In the episodes here the contestants play for over a half million pounds,
a figure which would be unattainable in a controlled experiment. Cooperation rates of close
to 50% are seen overall with some variation between groups. The identity of the opponent
has a role to play with less cooperation in games of the same gender and more cooperation
between players in the same age category. Overall, 17% of the money is left on the table
with mature players the most efficient at converting jackpots into winnings.
14
15. References
Beetsma, Roel M. W. J., and Peter C. Schotman, āMeasuring Risk Attitudes in a Natural
Experiment: Data from the Television Game Show Lingo,ā Economic Journal 111:474
(2001), 821ā848.
Berk, Jonathan B., Eric Hughson, and Kirk Vandezande, āThe Price Is Right, but Are the
Bids? An Investigation of Rational Decision Theory,ā American Economic Review 86:4
(1996), 954ā970.
Camerer, Colin F., Behavioural Game Theory: Experiments in Strategic Interaction, (2003)
Princeton, NJ: Princeton University Press.
Gertner, Robert, āGame Shows and Economic Behavior: Risk-Taking on Card Sharks,ā
Quarterly Journal of Economics 108:2 (1993), 507ā521.
Metrick, Andrew, āA Natural Experiment in Jeopardy!ā American Economic Review 85:1
(1995), 240ā253.
Ryan, Geraldine and Seamus Coffey, āGames of Strategy,ā Encyclopaedia of Decision
Making and Decision Support Technologies, Volume 2 (2006), 402-410.
15