This document presents a computational model for studying how digital consensus affects organizational design and performance. The model simulates decentralized autonomous organizations (DAOs) that make decisions through token-based voting, as well as traditional hierarchies and autonomous groups. Experiments show that in static environments, hierarchies perform best, DAOs outperform autonomous groups, and DAOs can outperform hierarchies in turbulent environments. Additional experiments demonstrate how voting thresholds, token asymmetry, and contributor incentives impact DAO performance.
This document outlines a study on decentralized autonomous organizations (DAOs) and consensus-based decision making. It presents a genetic algorithm to model organizational learning and performance in hierarchical versus consensus-based structures. The results show that digital consensus is slower to converge but maintains higher diversity, potentially outperforming hierarchies over time. Lower operational complexity favors consensus, while lower strategic complexity favors hierarchies. Environmental turbulence and token inequality can also impact performance.
Summary and conclusion - Survey research and design in psychologyJames Neill
This document provides an overview and summary of a lecture on survey research and design in psychology. It covers the following key points:
- Survey research involves using standardized questionnaires to collect data on psychological phenomena. It has become a popular social science method since the 1920s.
- Survey design considerations include whether the survey is self-administered or interview-based, the types of questions used, and response formats. Proper sampling and minimizing biases are also important.
- Analysis of survey data involves descriptive statistics, graphs, and correlations to describe and explore relationships in the data. Tools like exploratory factor analysis can be used to develop psychometric instruments. Multiple linear regression allows predicting outcomes from multiple variables.
The document discusses the diagnostic process in organizational development. It defines diagnosis as a systematic approach to understand an organization's present state by specifying the nature and causes of problems. The key stages of diagnosis are data gathering, identifying problem areas, interpreting the data, and developing potential solutions. Common diagnostic models described include differentiation-integration, sociotechnical systems, and force-field analysis. Steps in diagnosis involve collecting data, analyzing it, identifying problem areas, and assessing whether changes are addressing issues. The goal of diagnosis is to understand performance gaps and determine how to improve an organization's effectiveness.
This chapter discusses the foundations of group behavior in organizations. It defines different types of groups, examines models of group development, and explores how roles, norms, status, and cohesiveness influence group structure and performance. It also analyzes techniques for group decision-making and factors that determine a group's effectiveness.
Self-organization of society: fragmentation, disagreement, and how to overcom...Hiroki Sayama
This document summarizes a presentation on self-organization of society. It discusses how social fragmentation, disagreement and extremism can emerge from decentralized interactions between individuals seeking conformity and homophily. Three recent papers are summarized that show how social networks can become polarized through adaptive dynamics, how enhanced information gathering can intensify disagreement, and how behavioral diversity among individuals can allow for both cultural diversity and network connectivity in society. The key messages are that individual and collective outcomes may not align, and behavioral heterogeneity presents opportunities for diverse yet cohesive social outcomes.
Anticipation 2017 Assembling Requisite Stakeholder VarietyPeter Jones
This document discusses ensuring variety in stakeholder representation in foresight practices to reduce cognitive biases. It notes that foresight methods often mix to reduce reliance on one, but variety is also needed in stakeholder perspectives represented. Without accounting for cognitive and temporal biases in who is selected, four points of failure can occur: biased framing, biased content selection, horizon bias in stakeholders, and insufficient variety. The document advocates for evolutionary sampling to map categories related to the issue and minimize influence of biases, expanding variety both within the issue and beyond the future system. It also discusses accounting for individuals' temporal preferences to avoid horizon biases within groups.
Exploration and Exploitation in Organizational LearningNei Grando
This presentation considers the relation between the exploration of new possibilities and the exploitation of old certainties in organizational learning. It examines some complications in allocating resources between the two.
Adapting Test Teams to Organizational Power StructuresTechWell
Scapegoats, spin-doctors, white knights, and sycophants—have you found your test team playing these roles? Organizations, both large and small, often have distinct cultures and power structures with significant but insidious impact on how individual testers and teams are expected to operate. Sometimes the difference between doing what sponsors and stakeholders request and doing what is really needed becomes blurred. John Hazel helps you learn how to recognize the cultural characteristics of different types of software development teams, and how they drive expectations for the test team. Understand the decision-making dynamics and the perceived value of information across different organizational power structures, and the pitfalls that await unwary test teams. Develop strategies to adapt your team’s approach away from compliance and execution and toward discovery and dialogue. John shares his experiences across a spectrum of power structures, field-tested methods, and tools to help your team tailor their testing practice to add value while maintaining objectivity and impact.
This document outlines a study on decentralized autonomous organizations (DAOs) and consensus-based decision making. It presents a genetic algorithm to model organizational learning and performance in hierarchical versus consensus-based structures. The results show that digital consensus is slower to converge but maintains higher diversity, potentially outperforming hierarchies over time. Lower operational complexity favors consensus, while lower strategic complexity favors hierarchies. Environmental turbulence and token inequality can also impact performance.
Summary and conclusion - Survey research and design in psychologyJames Neill
This document provides an overview and summary of a lecture on survey research and design in psychology. It covers the following key points:
- Survey research involves using standardized questionnaires to collect data on psychological phenomena. It has become a popular social science method since the 1920s.
- Survey design considerations include whether the survey is self-administered or interview-based, the types of questions used, and response formats. Proper sampling and minimizing biases are also important.
- Analysis of survey data involves descriptive statistics, graphs, and correlations to describe and explore relationships in the data. Tools like exploratory factor analysis can be used to develop psychometric instruments. Multiple linear regression allows predicting outcomes from multiple variables.
The document discusses the diagnostic process in organizational development. It defines diagnosis as a systematic approach to understand an organization's present state by specifying the nature and causes of problems. The key stages of diagnosis are data gathering, identifying problem areas, interpreting the data, and developing potential solutions. Common diagnostic models described include differentiation-integration, sociotechnical systems, and force-field analysis. Steps in diagnosis involve collecting data, analyzing it, identifying problem areas, and assessing whether changes are addressing issues. The goal of diagnosis is to understand performance gaps and determine how to improve an organization's effectiveness.
This chapter discusses the foundations of group behavior in organizations. It defines different types of groups, examines models of group development, and explores how roles, norms, status, and cohesiveness influence group structure and performance. It also analyzes techniques for group decision-making and factors that determine a group's effectiveness.
Self-organization of society: fragmentation, disagreement, and how to overcom...Hiroki Sayama
This document summarizes a presentation on self-organization of society. It discusses how social fragmentation, disagreement and extremism can emerge from decentralized interactions between individuals seeking conformity and homophily. Three recent papers are summarized that show how social networks can become polarized through adaptive dynamics, how enhanced information gathering can intensify disagreement, and how behavioral diversity among individuals can allow for both cultural diversity and network connectivity in society. The key messages are that individual and collective outcomes may not align, and behavioral heterogeneity presents opportunities for diverse yet cohesive social outcomes.
Anticipation 2017 Assembling Requisite Stakeholder VarietyPeter Jones
This document discusses ensuring variety in stakeholder representation in foresight practices to reduce cognitive biases. It notes that foresight methods often mix to reduce reliance on one, but variety is also needed in stakeholder perspectives represented. Without accounting for cognitive and temporal biases in who is selected, four points of failure can occur: biased framing, biased content selection, horizon bias in stakeholders, and insufficient variety. The document advocates for evolutionary sampling to map categories related to the issue and minimize influence of biases, expanding variety both within the issue and beyond the future system. It also discusses accounting for individuals' temporal preferences to avoid horizon biases within groups.
Exploration and Exploitation in Organizational LearningNei Grando
This presentation considers the relation between the exploration of new possibilities and the exploitation of old certainties in organizational learning. It examines some complications in allocating resources between the two.
Adapting Test Teams to Organizational Power StructuresTechWell
Scapegoats, spin-doctors, white knights, and sycophants—have you found your test team playing these roles? Organizations, both large and small, often have distinct cultures and power structures with significant but insidious impact on how individual testers and teams are expected to operate. Sometimes the difference between doing what sponsors and stakeholders request and doing what is really needed becomes blurred. John Hazel helps you learn how to recognize the cultural characteristics of different types of software development teams, and how they drive expectations for the test team. Understand the decision-making dynamics and the perceived value of information across different organizational power structures, and the pitfalls that await unwary test teams. Develop strategies to adapt your team’s approach away from compliance and execution and toward discovery and dialogue. John shares his experiences across a spectrum of power structures, field-tested methods, and tools to help your team tailor their testing practice to add value while maintaining objectivity and impact.
This document summarizes a research study on complex problem solving using a computer simulation called Syntex. The study involved 54 students from Zhejiang University divided into 18 groups. The groups made management decisions for a simulated company each month. The researchers found differences between the "best" and "worst" groups based on company performance. Best groups increased capital and hired more employees over time compared to worst groups. The researchers also analyzed decision making processes and information gathering between the groups. They explored perspectives including the problem solving process, group interactions, and cross-cultural differences. The document discusses research design, validity, variables measured, and limitations of generalizing the results.
This document provides an introduction to participatory systemic inquiry. It discusses key concepts of systems thinking like nonlinearity, emergence and attractors. It emphasizes the need for iterative learning when dealing with complex systems. The document outlines principles of participatory systemic inquiry such as using multiple perspectives and methods, collective scrutiny through system mapping, and understanding dynamics and change. It describes creating system maps to capture issues, stakeholders, relationships, assumptions and dynamics in one place. The goal is to analyze maps to discern patterns and opportunities for leverage in order to engage the system through quick wins, shaping interventions and ongoing inquiry.
Shinsuke Sakuma (Waseda Univ.), Yusuke Goto (Iwate Pref. Univ.), and Shingo Takahashi (Waseda Univ.)
Analysis of Knowledge Retrieval Heuristics Considering Member's Load Balancing
The 3rd World Congress on Social Simulation
September 9, 2010 (Kassel, Germany)
Dual Approaches for Integrating Ethics into the Information Systems CurriculumACBSP Global Accreditation
Participants will experience and compare two approaches to introducing ethics into the MIS curriculum. Organizers, experienced in teaching ethics, will help participants evaluate different pedagogical options in terms of the needs and challenges of specific academic programs.
KnowMe and ShareMe: understanding automatically discovered personality traits...Leon Gou
The document summarizes a study that aimed to understand personality traits derived from social media text and users' preferences for sharing those traits. The study involved:
1. Automatically deriving users' Big 5 personality traits, fundamental needs, and basic values from social media posts using psycholinguistic analytics.
2. Validating the derived traits against users' self-reports and psychometric tests, finding correlations over 80% of the time.
3. Surveying users about sharing preferences for the derived traits in the workplace. Preferences depended on trait type, value, recipient, and the user's own traits.
4. Most users saw benefits but also risks to sharing, and suggested controls like transparency,
Hcic muller guha davis geyer shami 2015 06-29Michael Muller
Grounded theory and machine learning methods have more similarities than initially expected. Both approaches involve modeling theories or descriptions up from the data through an iterative process of constant comparison between the emerging theory/description and the data. They also both involve modeling down from a priori premises by applying theorized categories or relationships to the data and refining them based on how well they fit the data. A key difference is that grounded theory aims to develop theory without prematurely imposing categories, while machine learning often involves applying theorized categories or relationships to data from the beginning.
Only TeamQuest Combines 15+ years of Proven Success in Recruitment, Team Building & Coaching with an Award-Winning, Bias-Free, Innovation called Teamability® tech.
What Sets TeamQuest Advisors Apart?
We believe business is a Team Sport; we walk the talk by applying Teamability tech. Teamability measures the # 1 Performance Indicator: How People Will Perform in Teams.
This incredible new tech, at the centre of our approach, uncovers root causes to problems and provides highly relevant information; enabling effective strategies and actions that dramatically improve individual, team and company performance!
Business results depend on how people ‘team’ with each other that’s why TeamQuest solutions are based on ‘team performance metrics’.
This document is the oral defense presentation for Claude B. Tanoe's quantitative study examining the relationships between senior leader attrition, leadership competency, and organizational effectiveness in the federal government. The presentation outlines the problem statement, purpose, theoretical framework, research design, questions, population/sample, data collection/analysis, findings, conclusions and implications, limitations, and recommendations of the study. It also includes an introduction, outline, and concludes by asking for any questions.
The Formation of Job Referral Networks: Evidence from a Field Experiment in U...essp2
1) The study examines how job referral networks form in urban Ethiopia through a field experiment.
2) The experiment tests whether people link to others for self-regarding reasons like getting referrals, or other-regarding reasons like helping others get jobs.
3) Results show people in self-interest treatments linked to less connected others for self-interested reasons like getting referrals. But in other-regarding treatments, people did not link to help others.
4) The study suggests policies could encourage employers to ask referrals from a more diverse range of people to strengthen peripheral groups' network positions.
System Thinking - Affect on Decision MakingMuhammad Awais
The document discusses systems thinking and its impact on decision making. It begins with introductions to systems concepts and definitions of systems thinking. It describes the difference between system 1 and system 2 thinking, with system 1 being fast, automatic thinking and system 2 being slower, effortful thinking. It emphasizes that in today's complex and interconnected world, systems thinking is needed to understand complex problems and avoid unintended consequences of decisions. Systems thinking provides a holistic view rather than a narrow, reductionist view to help make better decisions. The document provides examples of applying systems thinking in various domains and argues it is a new way of thinking needed to address challenges of the current century.
Complexity in Ambiguous Problem Solution Search: Group Dynamics, Search Tac...Dr. Elliot Bendoly
This document summarizes an experiment on problem complexity, group synergy and performance. It found:
1) Nominal groups generally outperformed collaborative groups on more complex tasks, as complexity makes group benefits less clear.
2) Groups of specialists performed significantly better than generalist groups in both nominal and collaborative settings, especially on complex tasks.
3) In the first problem-solving period, intelligent search coverage of the solution space, rather than number of solutions, best predicted performance for individuals and specialists. Production blocking hindered collaborative generalist groups.
This is research project conduct in Larkana city in the course of organizational development to see the impact of organizational justice on employee turnover intention.
Engineering design of an environmental management system: A trans-disciplinar...Henk (Jan) Roodt
The big issues we face today contain society, nature and man-made components. It is difficult to consider these systems within the traditional systems contexts, where we can set well-defined boundaries during the design (analytical decomposition) process. Still, the analysis/synthesis process must be thorough enough to ensure that the functional, physical and allocated architectures that are discovered and defined during the analytical phase, can deliver a reasonable, traceable outcome.
How to conduct a social network analysis: A tool for empowering teams and wor...Jeromy Anglim
Slides and details available at: http://jeromyanglim.blogspot.com/2009/10/how-to-conduct-social-network-analysis.html
A talk on using social network analysis as a team development tool.
This document provides an introduction to system dynamics concepts. It discusses that systems are complex and interconnected, and interventions in one part can have unintended consequences in other parts. System dynamics uses feedback loops and computer simulations to model complex social and technical systems over time. Mental models, delays, and non-linear feedbacks can all contribute to unexpected and counterintuitive system behaviors. System dynamics is an interdisciplinary approach that can help address complex problems by taking a holistic, long-term view of systems and policies.
The document proposes a Group Selection pattern to optimize the formation and evolution of virtual organizations (VOs) in grid computing environments. The pattern biases agent interactions and migration based on group identifiers, allowing groups to evolve over time into optimized configurations through a process analogous to biological group selection. Experiments applying this pattern to a VO policy alignment scenario show it can evolve a large number of small, dynamic VOs into outcomes with high coordination and utility.
From data to endgame for dissertation and theses: what does it takeDoctoralNet Limited
Both studies analyzed large amounts of qualitative and quantitative data to understand organizational change and cross-cultural mentoring relationships. The first study examined technology changes in renewable energy companies using interviews, public records, and statistical analysis to determine how cognitive flexibility at the team and organization levels impacted the likelihood and scale of changes. The second study used grounded theory and interviews to develop a model of effective practices for mentors in cross-cultural relationships. Both analyzed data dimensions and levels to answer research questions and proposed theoretical frameworks to understand their topics of study.
This document discusses 10 examples of using network analysis techniques in various domains:
1. Using social network analysis to map the workforce and labor supply as a complex system.
2. Analyzing social network and interest graph data to power future shopping through identifying customer segments and influencers.
3. Using social network diagrams by drug marketers to locate influential doctors by identifying prescribing patterns and relationships between doctors.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
This document summarizes a research study on complex problem solving using a computer simulation called Syntex. The study involved 54 students from Zhejiang University divided into 18 groups. The groups made management decisions for a simulated company each month. The researchers found differences between the "best" and "worst" groups based on company performance. Best groups increased capital and hired more employees over time compared to worst groups. The researchers also analyzed decision making processes and information gathering between the groups. They explored perspectives including the problem solving process, group interactions, and cross-cultural differences. The document discusses research design, validity, variables measured, and limitations of generalizing the results.
This document provides an introduction to participatory systemic inquiry. It discusses key concepts of systems thinking like nonlinearity, emergence and attractors. It emphasizes the need for iterative learning when dealing with complex systems. The document outlines principles of participatory systemic inquiry such as using multiple perspectives and methods, collective scrutiny through system mapping, and understanding dynamics and change. It describes creating system maps to capture issues, stakeholders, relationships, assumptions and dynamics in one place. The goal is to analyze maps to discern patterns and opportunities for leverage in order to engage the system through quick wins, shaping interventions and ongoing inquiry.
Shinsuke Sakuma (Waseda Univ.), Yusuke Goto (Iwate Pref. Univ.), and Shingo Takahashi (Waseda Univ.)
Analysis of Knowledge Retrieval Heuristics Considering Member's Load Balancing
The 3rd World Congress on Social Simulation
September 9, 2010 (Kassel, Germany)
Dual Approaches for Integrating Ethics into the Information Systems CurriculumACBSP Global Accreditation
Participants will experience and compare two approaches to introducing ethics into the MIS curriculum. Organizers, experienced in teaching ethics, will help participants evaluate different pedagogical options in terms of the needs and challenges of specific academic programs.
KnowMe and ShareMe: understanding automatically discovered personality traits...Leon Gou
The document summarizes a study that aimed to understand personality traits derived from social media text and users' preferences for sharing those traits. The study involved:
1. Automatically deriving users' Big 5 personality traits, fundamental needs, and basic values from social media posts using psycholinguistic analytics.
2. Validating the derived traits against users' self-reports and psychometric tests, finding correlations over 80% of the time.
3. Surveying users about sharing preferences for the derived traits in the workplace. Preferences depended on trait type, value, recipient, and the user's own traits.
4. Most users saw benefits but also risks to sharing, and suggested controls like transparency,
Hcic muller guha davis geyer shami 2015 06-29Michael Muller
Grounded theory and machine learning methods have more similarities than initially expected. Both approaches involve modeling theories or descriptions up from the data through an iterative process of constant comparison between the emerging theory/description and the data. They also both involve modeling down from a priori premises by applying theorized categories or relationships to the data and refining them based on how well they fit the data. A key difference is that grounded theory aims to develop theory without prematurely imposing categories, while machine learning often involves applying theorized categories or relationships to data from the beginning.
Only TeamQuest Combines 15+ years of Proven Success in Recruitment, Team Building & Coaching with an Award-Winning, Bias-Free, Innovation called Teamability® tech.
What Sets TeamQuest Advisors Apart?
We believe business is a Team Sport; we walk the talk by applying Teamability tech. Teamability measures the # 1 Performance Indicator: How People Will Perform in Teams.
This incredible new tech, at the centre of our approach, uncovers root causes to problems and provides highly relevant information; enabling effective strategies and actions that dramatically improve individual, team and company performance!
Business results depend on how people ‘team’ with each other that’s why TeamQuest solutions are based on ‘team performance metrics’.
This document is the oral defense presentation for Claude B. Tanoe's quantitative study examining the relationships between senior leader attrition, leadership competency, and organizational effectiveness in the federal government. The presentation outlines the problem statement, purpose, theoretical framework, research design, questions, population/sample, data collection/analysis, findings, conclusions and implications, limitations, and recommendations of the study. It also includes an introduction, outline, and concludes by asking for any questions.
The Formation of Job Referral Networks: Evidence from a Field Experiment in U...essp2
1) The study examines how job referral networks form in urban Ethiopia through a field experiment.
2) The experiment tests whether people link to others for self-regarding reasons like getting referrals, or other-regarding reasons like helping others get jobs.
3) Results show people in self-interest treatments linked to less connected others for self-interested reasons like getting referrals. But in other-regarding treatments, people did not link to help others.
4) The study suggests policies could encourage employers to ask referrals from a more diverse range of people to strengthen peripheral groups' network positions.
System Thinking - Affect on Decision MakingMuhammad Awais
The document discusses systems thinking and its impact on decision making. It begins with introductions to systems concepts and definitions of systems thinking. It describes the difference between system 1 and system 2 thinking, with system 1 being fast, automatic thinking and system 2 being slower, effortful thinking. It emphasizes that in today's complex and interconnected world, systems thinking is needed to understand complex problems and avoid unintended consequences of decisions. Systems thinking provides a holistic view rather than a narrow, reductionist view to help make better decisions. The document provides examples of applying systems thinking in various domains and argues it is a new way of thinking needed to address challenges of the current century.
Complexity in Ambiguous Problem Solution Search: Group Dynamics, Search Tac...Dr. Elliot Bendoly
This document summarizes an experiment on problem complexity, group synergy and performance. It found:
1) Nominal groups generally outperformed collaborative groups on more complex tasks, as complexity makes group benefits less clear.
2) Groups of specialists performed significantly better than generalist groups in both nominal and collaborative settings, especially on complex tasks.
3) In the first problem-solving period, intelligent search coverage of the solution space, rather than number of solutions, best predicted performance for individuals and specialists. Production blocking hindered collaborative generalist groups.
This is research project conduct in Larkana city in the course of organizational development to see the impact of organizational justice on employee turnover intention.
Engineering design of an environmental management system: A trans-disciplinar...Henk (Jan) Roodt
The big issues we face today contain society, nature and man-made components. It is difficult to consider these systems within the traditional systems contexts, where we can set well-defined boundaries during the design (analytical decomposition) process. Still, the analysis/synthesis process must be thorough enough to ensure that the functional, physical and allocated architectures that are discovered and defined during the analytical phase, can deliver a reasonable, traceable outcome.
How to conduct a social network analysis: A tool for empowering teams and wor...Jeromy Anglim
Slides and details available at: http://jeromyanglim.blogspot.com/2009/10/how-to-conduct-social-network-analysis.html
A talk on using social network analysis as a team development tool.
This document provides an introduction to system dynamics concepts. It discusses that systems are complex and interconnected, and interventions in one part can have unintended consequences in other parts. System dynamics uses feedback loops and computer simulations to model complex social and technical systems over time. Mental models, delays, and non-linear feedbacks can all contribute to unexpected and counterintuitive system behaviors. System dynamics is an interdisciplinary approach that can help address complex problems by taking a holistic, long-term view of systems and policies.
The document proposes a Group Selection pattern to optimize the formation and evolution of virtual organizations (VOs) in grid computing environments. The pattern biases agent interactions and migration based on group identifiers, allowing groups to evolve over time into optimized configurations through a process analogous to biological group selection. Experiments applying this pattern to a VO policy alignment scenario show it can evolve a large number of small, dynamic VOs into outcomes with high coordination and utility.
From data to endgame for dissertation and theses: what does it takeDoctoralNet Limited
Both studies analyzed large amounts of qualitative and quantitative data to understand organizational change and cross-cultural mentoring relationships. The first study examined technology changes in renewable energy companies using interviews, public records, and statistical analysis to determine how cognitive flexibility at the team and organization levels impacted the likelihood and scale of changes. The second study used grounded theory and interviews to develop a model of effective practices for mentors in cross-cultural relationships. Both analyzed data dimensions and levels to answer research questions and proposed theoretical frameworks to understand their topics of study.
This document discusses 10 examples of using network analysis techniques in various domains:
1. Using social network analysis to map the workforce and labor supply as a complex system.
2. Analyzing social network and interest graph data to power future shopping through identifying customer segments and influencers.
3. Using social network diagrams by drug marketers to locate influential doctors by identifying prescribing patterns and relationships between doctors.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
1. Meta-Organizational Learning Through Digital Consensus
Junyi Li1
, Jungpil Hahn1
, Giri Kumar Tayi2
July 19, 2023
1Department of Information Systems and Analytics, National University of Singapore
2School of Business, State University of New York at Albany
4. Emergence of DAOs
• Decentralized Autonomous Organizations (DAOs): making organizational decision
by token-based voting.
• Meta-Oganization: legally autonomous entities [2]
2
5. Paradoxes in Organizational Design
1. Will DAOs (digital consensus) be an effective mode of decentralized governance?
2. Traditional paradoxes:
• Control vs. Flexibility [3]
• Centralization vs. Decentralization [1]
• Authority vs. Self-Organizing [7]
3
6. Research Question
How will digital consensus affect organizational design?
1. In what ways does consensus-based governance differ from a traditional one,
namely autonomy, and/or hierarchy (what is)?
2. Under what circumstances does consensus-based governance outperform other
modes (what might be)?
3. What is the underlying mechanism accounting for any performance differences
(how)?
4
11. Hierarchy
• Individuals learn from higher performing connected peers with probability η
• Top-Down Coordination: individuals’ solutions must be coherent with managers’
policy
8
12. DAO
• Individuals learn from higher performing connected peers with probability η
• Bottom-Up Coordination: individuals’ solutions must be coherent with policy
consensus
9
14. Experiment 1: Baseline
• Static Reality
• Best Hierarchies as in March’s
model [4]
• Naı̈ve DAOs
• simple majority rule
• uniform token distribution
• full engagement
10
15. Experiment 1: Baseline
Investigating performance variance adds insights.
• DAOs evolve through a staggered process of polarization and homogenization, as
opposed to autonomies’ continuous polarization and hierarchies’ continuous
homogenization. (What is & How?)
11
16. Experiment 2: Turbulence
The performance reversal delays as environments become less turbulent (more static).
“Turbulent” ← → “Static”
12
17. Summary of Baseline
Proposition 1a
In static environments, hierarchies outperform DAOs, and DAOs outperform autonomies.
Proposition 1b
In turbulent environments, DAOs tend to outperform hierarchies and autonomies.
13
18. Summary of Extensions
Proposition 2
Voting threshold has an inverted U-shaped impact on DAOs’ performance.
Proposition 3
Token asymmetry has a negative impact on DAOs’ performance.
Proposition 4a
Contributor incentive has a positive impact on DAOs’ performance when the inactive
rate is high.
Proposition 4b
Contributor incentive has a negative impact on DAOs’ performance when the inactive
rate is low.
14
20. Key Takeaways
? Power A vs. Power B
✓ Polarization vs. Homogenization
? Optimize DAOs’ design
✓ By voting threshold, token asymmetry, and contributor incentive
15
24. Digital Consensus
Digital consensus herein refers to algorithmic coordination rules that are automatically
executed in distributed computer systems and are only modifiable via voting.
• Transparent
• Democratically editable
• Self-executing
26. Genetic Algorithm
m payoff function in March’s (1991) model [4]:
Φ(x) =
m
X
j=1
δj (1)
where δj = 1 if xj corresponds with reality on dimension j; δj = 0 otherwise.
Belief diversity is measured as follows:
Belief Diversity =
2
mn(n − 1)
1
2
n(n−1)
X
i=1
m
X
j=1
ωij (2)
where ωij = 1 if two chosen individuals in the pair i have different beliefs on dimension
j; ωij = 0 otherwise.
27. Model Parameters
Parameter Remark Range of parameter Default
m Problem dimension 60, 90, 120, 150 90
n Number of individuals in the organization 280, 350, 420, 490 350
z The group size of the autonomous group 7, 14, 28 7
α The aggregation degree 1, 3, 5 3
η The learning rate of individuals 0.1 ∼ 0.9 0.3
p1 The probability of learning from the code (Hierarchy) 0.1 ∼ 0.9 0.1
p2 The probability of learning by the code (Hierarchy) 0.1 ∼ 0.9 0.9
p3 The probability of individual reconfiguration 0 ∼ 0.5 0
n′ The number of managers (Hierarchy) 40, 50, 60, 70 50
θ The voting threshold to pass a protocol as consensus 0.4 ∼ 0.7 0.5
Ttb The period of environmental turbulence 20 ∼ 100 -
Itb The intensity of environmental turbulence 0, 10%, 20% 0
ρ The shape parameter in Pareto distribution 1, 2, 3 -
γ Incentive degree & reactivation rate 0 ∼ 0.9 0
pi The probability of individuals being inactive in voting 0 ∼ 0.9 0
Repetition The number of simulation runs 500 500
Search Round The iteration of search in one simulation run 300, 500, 1000, 2000 -
Notes: Search rounds are verified by pilot tests. The default values correspond to the usage in the literature.
28. Baseline & Turbulence: Conclusion
Proposition 1a
In static environments, hierarchies outperform DAOs, and DAOs outperform
autonomies.
Proposition 1b
In turbulent environments, DAOs tend to outperform hierarchies and autonomies.
29. Experiment 3: Voting Threshold
• Voting threshold has an inverted U-shaped effect on DAOs’ performance.
30. Experiment 3: Voting Threshold
• Staggered process: polarization before consensus, homogenization after consensus.
• The staggered process can be adjusted by θ.
31. Experiment 4: Token Asymmetry
• Asymmetry restrains the polarization before consensus formation.
• Causing homogenization into minority views.
32. Experiment 5: Contributor Incentive
• Due to the intense reward, there will be additional asymmetry, resulting in worse
performance (–).
• Due to reactivation, incentives are beneficial when individuals rarely vote (+).
35. Sensitivity Analysis
• We conduct sensitivity analyses for non-focus parameters, including the group size
z, the number of individuals n, the number of managers n′, the aggregation level
α, the learning rate η, and the reality dimension m. Qualitative results are
consistent across these parameter values.
• A variety of hierarchies with varying supervision efficiencies are also examined. In
particular, the hierarchies’ performance will linearly decrease with increasing p1 or
decreasing p2, consistent with March’s (1991) model. This means naı̈ve DAOs
certainly outperform some deficient hierarchies.
• With individual reconfiguration rate p3 [8], the performance of DAOs and
hierarchies largely restore to static levels. Yet, autonomies may outperform when
p3 > 0.2, resonating with the literature that autonomous teams excel in intensive
organizational experimentation [5, 6].
36. Robustness: Supervision (i)
• p1: learning from code, p2: learning by code
• Left side: p2 = 0.9, right side: p1 = 0.1
• These two figures represent two specific segment lines across the entire 3D surface
(p1, p2, Z)
40. References i
[1] Christina Fang, Jeho Lee, and Melissa A. Schilling.
Balancing Exploration and Exploitation Through Structural Design: The
Isolation of Subgroups and Organizational Learning.
Organization Science, 21(3):625–642, June 2010.
[2] Ranjay Gulati, Phanish Puranam, and Michael Tushman.
Meta-Organization Design: Rethinking Design in Interorganizational and
Community Contexts.
Strategic Management Journal, 33(6):571–586, 2012.
41. References ii
[3] Daniel A. Levinthal and Maciej Workiewicz.
When Two Bosses Are Better Than One: Nearly Decomposable Systems
and Organizational Adaptation.
Organization Science, 29(2):207–224, 2018.
[4] James G. March.
Exploration and Exploitation in Organizational Learning.
Organization Science, 2(1):71–87, February 1991.
[5] Peerasit Patanakul, Jiyao Chen, and Gary S. Lynn.
Autonomous Teams and New Product Development.
Journal of Product Innovation Management, 29(5):734–750, 2012.
42. References iii
[6] Phanish Puranam, Harbir Singh, and Maurizio Zollo.
Organizing for Innovation: Managing the Coordination-Autonomy Dilemma
in Technology Acquisitions.
Academy of Management Journal, 49(2):263–280, April 2006.
[7] Marlo Raveendran, Phanish Puranam, and Massimo Warglien.
Division of Labor through Self-Selection.
Organization Science, 33(2):810–830, 2022.
43. References iv
[8] Timo Sturm, Jin Gerlacha, Luisa Pumplun, Neda Mesbah, Felix Peters, Christoph
Tauchert, Ning Nan, and Peter Buxmann.
Coordinating Human and Machine Learning for Effective Organization
Learning.
MIS Quarterly, 45(3):1581–1602, September 2021.