Ilustração retangular dividida verticalmente em duas metades representando a dualidade entre o pensamento humano e a inteligência artificial. Do lado esquerdo, uma silhueta de cabeça humana preenchida com engrenagens, labirintos e símbolos de tomada de decisão, em tons quentes de laranja e amarelo, evocando criatividade e reflexão. Do lado direito, uma representação estilizada de um cérebro digital com fluxos de dados, códigos binários e circuitos em cores frias de azul e roxo, simbolizando a tecnologia e a precisão da inteligência artificial. No centro, uma transição suave mescla os elementos, reforçando a integração entre os dois mundos. O fundo inclui padrões abstratos de redes neurais e ondas cerebrais, enfatizando o tema da cognição e do diálogo entre sistemas.

The Enhanced Reflection of ChatGPT o1 and Human Thought: A New Perspective on Decision and Technology

Days ago, I listed HERE a series of characteristics of the o1 version of ChatGPT with the aim of drawing a parallel with the human decision-making process, especially as described by Daniel Kahneman in his book Thinking, Fast and Slow. However, I have now decided to write a more essay-like text on the subject.


Artificial intelligence (AI) is increasingly present in our daily lives, influencing everything from internet searches to virtual assistants on our devices. The latest evolution in this field is ChatGPT o1, an enhanced version of OpenAI’s language model, which stands out for “reflecting” more deeply before responding. This feature not only improves the quality of interactions but also allows us to draw interesting parallels with the human decision-making process, especially as described by Daniel Kahneman in his book Thinking, Fast and Slow”.

Kahneman, awarded the Nobel Prize in Economics, explores how our mind operates through two systems: System 1, which is fast, automatic, and intuitive, and System 2, which is slower, deliberative, and analytical. While System 1 allows us to react instantly to stimuli, it is in System 2 that deep reflection occurs, essential for complex and rational decisions.

ChatGPT o1 seems to incorporate this dynamic. By “reflecting” more before generating responses, the model is, in a certain way, engaging in a process similar to the human System 2. This results in more precise, contextualized, and useful answers, avoiding pitfalls that previous models, operating more automatically, could face.

This comparison raises important questions about how we interact with technology and how AI can be designed to complement our cognitive processes. If AI models can be developed to reflect more deeply, they can assist in making complex decisions, offering more balanced analyses and mitigating common biases.

However, it is crucial to recognize the fundamental differences between human thought and computational processing. While our mind is influenced by emotions, personal experiences, and social contexts, AI operates based on algorithms and data patterns. Therefore, although ChatGPT o1 can simulate a process of reflection, it does not possess consciousness or genuine understanding.

This distinction does not diminish the potential of AI but highlights the importance of using it as a tool that complements, not replaces, human judgment. Models like ChatGPT o1 can help us see beyond our own biases, offering additional perspectives and relevant information that enrich our ability to make informed decisions.

Moreover, the incorporation of more “reflective” processes in AI invites us to reconsider how we develop these technologies. The emphasis should not be only on speed and efficiency but also on the quality and depth of responses. This is particularly relevant in sensitive areas such as health, finance, and education, where informed decisions are crucial.

In summary, the advancement represented by ChatGPT o1 is a significant step in the evolution of artificial intelligence. By bringing machine processing closer to our own way of thinking, a world of possibilities opens up for collaboration between humans and AI. However, it is up to us to ensure that this technology is developed and used ethically, responsibly, and aligned with human values.

Reflection is not just a cognitive function but also an invitation to critical awareness. Perhaps, by observing “reflection” in the machine, we may be inspired to deepen our own, recognizing that in an increasingly complex world, thinking slowly may be the key to better and wiser decisions.


Deoclécio Paiva de Castro

Ph.D student in Economics
M.Sc in Mathematical Optimization Modeling and Quantitative Methods
B.Sc in Industrial/Production Engineer

Ilustração retangular dividida verticalmente em duas metades representando a dualidade entre o pensamento humano e a inteligência artificial. Do lado esquerdo, uma silhueta de cabeça humana preenchida com engrenagens, labirintos e símbolos de tomada de decisão, em tons quentes de laranja e amarelo, evocando criatividade e reflexão. Do lado direito, uma representação estilizada de um cérebro digital com fluxos de dados, códigos binários e circuitos em cores frias de azul e roxo, simbolizando a tecnologia e a precisão da inteligência artificial. No centro, uma transição suave mescla os elementos, reforçando a integração entre os dois mundos. O fundo inclui padrões abstratos de redes neurais e ondas cerebrais, enfatizando o tema da cognição e do diálogo entre sistemas.

ChatGPT o1 and Human Rational Decision-Making According to Kahneman: A Technical and Scientific Analysis

The evolution of artificial intelligence models, especially in the field of natural language processing, has transformed the way we interact with technology. ChatGPT o1, an enhanced version of the language models developed by OpenAI, stands out for its ability to “reflect” more deeply before generating responses. This characteristic allows for an intriguing comparison with the process of human rational decision-making described by Daniel Kahneman in his book Thinking, Fast and Slow”. The book is based on decades of research in cognitive psychology and behavioral economics conducted by Kahneman and his collaborator Amos Tversky.

In this article, we will explore together the analogy between ChatGPT o1 and human decision-making in depth, connecting fundamental concepts and theories. We will enrich the discussion with technical information from key scientific articles that underpin Kahneman’s theories. Furthermore, we will discuss the technical and scientific implications of this comparison, examining how the slower and more deliberative processing of ChatGPT o1 resembles the human System 2, responsible for analytical and rational thinking.

Introduction

Artificial intelligence (AI) has advanced by leaps and bounds, especially in the domain of natural language processing (NLP). Models like ChatGPT have revolutionized the interaction between humans and machines, offering increasingly precise and contextually appropriate responses. The latest version, ChatGPT o1, incorporates the ability to “reflect” more deeply before responding, allowing for analyses with more detail and nuance.

This enhanced capacity for reflection leads us to a comparison with the human decision-making process. In “Thinking, Fast and Slow”, Daniel Kahneman describes two thinking systems that govern the human mind:

  1. System 1: Fast, automatic, and intuitive.
  2. System 2: Slow, deliberative, and analytical.

This distinction is crucial for understanding how we make decisions and how we can improve these processes. By exploring the similarities between ChatGPT o1 and the human System 2, we can gain insights into the development of AI models that more effectively emulate aspects of human thought.


Thinking Systems: What Are Systems 1 and 2?

To establish the connection between ChatGPT o1 and human thought, it’s essential to understand the two thinking systems described by Kahneman.

System 1: Fast and Intuitive Thinking

System 1 operates automatically and quickly, without voluntary effort or conscious control. It is responsible for innate skills, such as detecting that one object is farther away than another or completing the phrase “I only know that I don’t…”.

This system uses heuristics, which are mental shortcuts that simplify decision-making. Although useful, heuristics can lead to systematic errors or cognitive biases.

Heuristics and Biases of System 1:

  • Representativeness Heuristic: We assess the probability of an event based on how similar it is to an existing prototype in our minds. For example, if someone is described as introverted and methodical, we might assume they are a scientist, even if statistically it’s more likely they are a teacher. (Tversky & Kahneman, 1974)
  • Availability Heuristic: We estimate the frequency or probability of an event based on how easily examples come to mind. For example, after watching news about airplane accidents, we might overestimate the risk of flying. (Tversky & Kahneman, 1973)
  • Anchoring Bias: The tendency to rely too heavily on the first piece of information received (the anchor) when making subsequent decisions. If we’re asked whether the Amazon River is more or less than 6,000 km long, that initial information will influence our estimate. (Tversky & Kahneman, 1974)

These biases show that while System 1 is efficient, it can lead to irrational decisions.

System 2: Slow and Deliberative Thinking

System 2 comes into play when tasks require attention and mental effort, such as solving a complex mathematical equation or making important decisions. It is characterized by being more analytical and capable of abstract thinking.

Characteristics of System 2:

  • Critical Analysis: Evaluates information in detail, considering evidence and logic.
  • Cognitive Control: Can suppress or correct automatic impressions from System 1, avoiding errors and biases.
  • Abstract Thinking: Ability to deal with complex concepts and hypothetical reasoning.

For example, when deciding on a financial investment, System 2 would evaluate risks and benefits, consult data, and avoid impulsive decisions.


Prospect Theory: A New Perspective on Decisions Under Risk

Kahneman and Tversky challenged traditional economic models that assume people are rational agents seeking to maximize expected utility. In their article Prospect Theory: An Analysis of Decision under Risk (1979), they introduce Prospect Theory, which describes how people actually make decisions under risk.

Main Components of Prospect Theory:

  1. Value Function: People evaluate gains and losses relative to a reference point (usually the status quo), not in absolute terms. The value function is concave for gains and convex for losses, reflecting risk aversion in gains and risk-seeking in losses.
  2. Loss Aversion: Losses are felt more intensely than equivalent gains are appreciated. This means that the pain of losing $100 is greater than the pleasure of gaining $100. (Kahneman et al., 1991)
  3. Probability Weighting: People tend to overestimate low-probability events and underestimate high-probability events. This explains why lotteries are popular despite the low chances of winning. (Tversky & Kahneman, 1992)

Prospect Theory explains various anomalies observed in economic behavior, showing that human decisions are influenced by psychological factors.

To deepen our understanding, we can explore the studies that underpin Kahneman’s theories.

Representativeness Heuristic

In the study “Judgment under Uncertainty: Heuristics and Biases” (Tversky & Kahneman, 1974), the authors show how people judge the probability of an event based on how representative it is, ignoring relevant statistical information (base rates). For example, when reading a description of a person who likes poetry, one might assume they are a literature professor, even though there are more engineers in the population.

Availability Heuristic

In “Availability: A Heuristic for Judging Frequency and Probability” (Tversky & Kahneman, 1973), the authors demonstrate that the ease with which we can recall examples influences our perception of frequency. For instance, after news of violent crimes, we may believe that crime rates have increased, even if statistics show otherwise.

Loss Aversion and Endowment Effect

In the study “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias” (Kahneman et al., 1991), the exploration is on how people value the goods they own more (endowment effect) and are more motivated to avoid losses than to achieve equivalent gains. This explains behaviors like resistance to selling an item at market price.

Probability Weighting in Prospect Theory

In “Advances in Prospect Theory: Cumulative Representation of Uncertainty” (Tversky & Kahneman, 1992), the authors refine Prospect Theory by introducing the probability weighting function, which explains systematic deviations in how we perceive probabilities, especially in low- and high-probability events.


ChatGPT o1: A Deeper Approach to Language Processing

ChatGPT o1 was designed, according to OpenAI, to incorporate a deeper level of processing when generating responses. Unlike previous versions, which might respond quickly based on statistical associations, ChatGPT o1 “reflects” before responding, analyzing context and nuances more deeply.

Characteristics of ChatGPT o1:

  • Deep Contextual Processing: Considers a broader context, allowing it to understand nuances that might be missed in superficial analyses.
  • Multiple Internal Iterations: Performs several passes over the input data, refining understanding before producing an output.
  • Bias Reduction: Employs techniques to mitigate biases present in training data, aiming for more balanced responses.

This approach resembles the human System 2, where slow and deliberative thinking leads to more rational and considered decisions.


Parallels Between ChatGPT o1 and the Human Decision-Making System 2

The comparison between ChatGPT o1 and human System 2 reveals interesting similarities.

Deliberative and Deep Processing

  • Human (System 2): Requires conscious effort to analyze complex information, suppressing automatic responses from System 1.
  • ChatGPT o1: Uses additional computational resources to deeply analyze context, avoiding responses based solely on quick statistical associations.

For example, when answering a complex question, ChatGPT o1 will consider multiple possible interpretations before formulating a response.

Mitigation of Cognitive Biases

  • Human: System 2 can identify and correct biases generated by System 1, such as the availability heuristic.
  • ChatGPT o1: Implements techniques to detect and reduce biases learned during training, providing fairer and more accurate responses.

This is crucial because AI models trained on large datasets can inadvertently learn and reproduce biases present in those data.

Critical Analysis and Reflection

  • Human: Ability to question assumptions, consider evidence, and think critically about information.
  • ChatGPT o1: Analyzes information more critically, taking into account different perspectives and contexts before responding.

For example, when asked to discuss a controversial topic, ChatGPT o1 can present multiple points of view, reflecting a more balanced understanding.


Technical and Scientific Implications

The comparison between ChatGPT o1 and human thought has several implications.

Advances in Artificial Intelligence

The ability of ChatGPT o1 to “reflect” more deeply represents a significant advancement in AI. It suggests that models can be designed to emulate aspects of human thought, improving the quality of interactions.

  • Layered Processing: Using multiple layers of processing allows the model to capture nuances and complex contexts.
  • Adaptive Learning: Models can be trained to recognize and correct their own errors, similar to the functioning of System 2.

Challenges and Limitations

However, it’s important to recognize the fundamental differences between human and computational processing.

  • Absence of Consciousness: ChatGPT o1 does not possess consciousness or subjective experiences; its processing is based on statistical patterns.
  • Data Dependence: The model’s performance is limited by the quality and scope of training data.
  • Interpretation vs. Association: While humans interpret information based on deep semantic understanding, the model works with learned associations.

Ethics and Responsibility

The development of advanced models raises ethical questions.

  • Transparency: It’s essential that systems are transparent in their functioning, allowing users to understand how responses are generated.
  • Algorithmic Bias: Developers must be attentive to biases present in data and work actively to mitigate them.
  • Social Impact: How AI models influence society must be carefully considered, ensuring that benefits are widely distributed.

Practical Applications and Future Perspectives

Understanding the parallels between ChatGPT o1 and human thought opens up new possibilities.

Improvement in Human-Machine Interaction

  • Advanced Chatbots: Models capable of better understanding context can provide more effective support in areas like education, health, and customer service.
  • Personalization: The ability to reflect allows models to tailor responses to users’ specific needs.

For example, a virtual assistant can offer more accurate recommendations if it understands nuances in the user’s preferences.

Support for Decision-Making

  • Virtual Assistants: Can help people make more informed decisions by providing detailed analyses and identifying possible biases.
  • Educational Tools: Can assist in teaching critical thinking and problem-solving skills.

This is especially useful in academic environments, where students can benefit from personalized feedback.

Ethical and Social Challenges

  • Inclusion and Diversity: Ensuring that models consider a wide range of perspectives is fundamental to avoid perpetuating prejudices.
  • Transparency and Trust: Users need to trust that models are fair and understand how they work.

For example, in medical applications, it’s crucial that professionals understand how an AI arrived at a particular recommendation.


Conclusion

The enhanced reflection of ChatGPT o1 represents a significant advancement in the quest for more effective and precise artificial intelligence models. By comparing this behavior with the human rational decision-making process described by Kahneman, we gain valuable insights into how deeper processing can improve outcomes in both cases.

Although there are fundamental differences between human and computational processing, incorporating principles such as bias mitigation and deliberative analysis into AI models brings us closer to developing systems that not only respond quickly but also offer high-quality and relevant answers.

This interdisciplinary analysis contributes to the development of more effective AI systems and to the enhancement of human decision-making. By better understanding our own cognitive processes and how they can be reflected in advanced technologies, we can move toward a more harmonious and productive interaction between humans and machines.


References

  • Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80(4), 237-251.
  • Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207-232.
  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.
  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.
  • Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), 193-206.
  • Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323.
  • Kahneman, D. (2011). Rápido e Devagar: Duas Formas de Pensar. Objetiva.

Deoclécio Paiva de Castro

Ph.D student in Economics
M.Sc in Mathematical Optimization Modeling and Quantitative Methods
B.Sc in Industrial/Production Engineer

analise de sentimento nas eleições de fortaleza Sarto Evandro Leitão andre fernandes Eduardo Girão geroge lima

Artificial Intelligence in Electoral Analysis: Media Sentiment in Fortaleza, Ceará, Brazil

In recent years, technology has been transforming the way we analyze data and make decisions, especially in areas like politics and electoral campaigns. Although this project is recent, it is the result of a long journey that I have been building for years, working with social networkselectoral campaigns, and government. From 2008 to 2016, I participated in successful campaigns in Ceará, including Fortaleza, and helped create strategies that were already beginning to consider the influence of the internet on politics. The analysis of “sentiment” on social networks, which at the time required considerable manual effort, was a pioneering practice in which I had the privilege to work. Today, I am applying this experience with a more technological and strategic approach through artificial intelligence. We conducted an Electoral Sentiment Analysis with AI.

This personal project is a direct evolution of that experience. It arose from my academic interest in using internet data to identify decision-makers’ preferences, applying these insights in game theory models. Sentiment analysis of electoral news was a natural step within this context, and by focusing on the 2024 Fortaleza elections, we were able to generate a detailed report on how the main candidates were perceived throughout the campaign.


From Idea to Implementation

The conception of this project is deeply rooted in my journey with political campaigns and social media management. Back in 2010, when I began working more intensively on using the internet for campaigns, sentiment analysis was done manually, with teams analyzing each content to understand how messages resonated. This experience taught me the value of understanding the audience and adjusting strategies based on real data.

Today, with artificial intelligence, the process has evolved, becoming more agile and precise. The idea of using AI to monitor sentiment is not just technological but also strategic. Campaigns change rapidly, and having a tool that allows real-time adaptation to changes in voter perception is a competitive advantage. Seeing AI automatically classify mentions of candidates as positive, negative, or neutral was the result of a vision I’ve been cultivating for a long time.


Sentiment Analysis and Academic Applications

My academic motivation has always been to better understand how data can reveal decision-makers’ preferencesGame theory, in which I am deeply interested, can be enriched by these analyses of data extracted from the internet. With the monitoring of real-time sentiments, we can predict behaviors, map future scenarios, and create more accurate models to understand how individuals and groups make decisions.

The sentiment analysis project for the Fortaleza elections is a clear example of how these concepts can be applied. Utilizing AI, we analyzed more than 1,132 news articles, collected from 56 media outlets, and identified how the candidates were perceived over time. The mentions were classified as positive, neutral, or negative, allowing deep insights to be generated from the data.

*Sorry about graphs in Portuguese

This pie chart illustrates the percentage division of mentions for each candidate. Capitão Wagner and Evandro Leitãowere the candidates with the highest presence on news portals, representing over 22% each. On the other hand, Eduardo Girão and George Lima had less prominence, with significantly lower percentages, indicating less media exposure. Sarto had only 13.07% of space on news portals.
This graph shows the number of news articles published by different media outlets. The website of Grupo O Povo was the one that published the most about the candidates, followed by Diário do NordesteGCmais, and Grupo Globo. This highlights which media outlets had the most participation in the election coverage, which can influence public perception of the candidates.

The Generated Report: A Portrait of the Elections in Fortaleza

The report generated by the project provided a clear view of how the candidates were perceived during the campaign. Mentions of Evandro Leitão and Capitão Wagner showed a predominance of positive perceptions, while candidates like Eduardo Girão faced a greater amount of negative mentions.

Furthermore, most mentions were classified as neutral, reflecting the informative nature of the news. However, the fluctuations between positive and negative sentiment helped show how specific campaign events directly influenced public perception. This type of analysis allows campaigns to adjust their strategies in real-time, according to the evolution of media coverage.


This graph presents the distribution of sentiments among positive, neutral, and negative for each candidate. The majority of mentions for all candidates are neutral, reinforcing the impartiality of informative news, such as coverage of agendas and electoral polls. The highlight here is that even with a large proportion of neutral mentions, negative sentiment is also notable in candidates like André Fernandes and Eduardo Girão.

This graph shows the proportion of positive and negative mentions for each candidate, excluding neutral mentions. We can observe that candidates like Evandro Leitão and Capitão Wagner had a higher percentage of positive mentions compared to candidates like Eduardo Girão and George Lima, who had a larger share of negative mentions. This may indicate how the media evaluates the performance or speeches of these candidates during the campaign.

Cumulative sentiment over time for candidate Evandro Leitão.

Cumulative sentiment over time for candidate André Fernandes.

Cumulative sentiment over time for candidate José Sarto.

The cumulative sentiment over time offers a detailed view of how candidates’ perceptions changed during the electoral campaign, highlighting the moments that led Evandro Leitão and André Fernandes to the second round and the fall of the current mayor, José Sarto, who failed to get re-elected. Evandro Leitão stood out with a clear trend of growth in positive sentiment. From the end of August, there was a steady rise until early October, indicating continuous positive response from the media and the public, which consistently consolidated his image throughout the campaign. André Fernandes, on the other hand, showed moderate fluctuations at the beginning of the race, with a sharp drop in cumulative sentiment in mid-September. However, he managed to recover in early October, with a slight increase in positive mentions, demonstrating a resilient campaign that overcame challenges in the final weeks. As for José Sarto, he showed a relatively stable sentiment until the end of September, when there was a significant negative shift starting on the 27th. This sharp decline may have been the result of more critical coverage, and the accumulated positive sentiment remained low, reflecting the difficulties his campaign faced in the final weeks.

The study considered the period between August 6 and October 5, 2024, covering the most intense weeks of the electoral campaign up to the final phase before the election. During this time, the public and media perception of the main candidates for the Mayor of Fortaleza was monitored, with sentiment analysis extracted from 1,132 news articlespublished in 56 media outlets.


Sentiment Analysis with AI: A System of Global Application

O que mais me entusiasma é que este projeto, embora focado nas eleições de Fortaleza, pode ser aplicado em diferentes What excites me most is that this project, although focused on the elections in Fortaleza, can be applied in different contexts and industries. The system we developed is capable of analyzing data on any subject, whether a political campaign or a brand’s reputation in the market. Companies can use this technology to monitor the public perception of their products or services, while public agencies can track how policies are being received.

The ability to monitor data in real-time, in different languages and markets, makes this system a tool of great strategic value for any organization that needs accurate and immediate insights about what is being said in its area of operation.

It is essential to emphasize that, despite the power of AI, it does not replace human work. Artificial intelligence is a tool that amplifies our analytical capacity, but it is still human critical thinking and creativity that provide the necessary context to transform this data into strategic actions.

This project is proof that technology can be an indispensable ally, but it is human work that gives meaning to data and transforms insights into concrete results. In the electoral field, or any other sector, the balance between technology and human intelligence will be the key to success.

Throughout this process, I also needed to reflect on the ethical issues involving data monitoring. Although we are dealing with public data and news sources, it is important to consider the impact that monitoring can have. Privacy and transparency are issues that are always present when we use AI for sentiment analysis. And in times of fake news and post-truth, the use of AI to detect patterns of misinformation is an increasing necessity.

In this project, we sought to ensure that all analyses were done ethically, and that the focus was on quality public data. Thus, the project not only provides valuable insights for political strategy but also acts transparently and responsibly in its use of data.

Finally…

This project reflects not only my academic interest in data and strategic decision-making, but also my years of experience working with electoral campaignssocial networks, and government. The generated report provides a detailed view of how the candidates in Fortaleza were perceived over time, and I believe this technology has an essential role in the future of data analysis.

Furthermore, the application of this technology goes far beyond elections. Whether in the political field, the corporate market, or the public sector, sentiment analysis and real-time data monitoring offer valuable insights for any organization that needs to adjust its strategies based on public perceptions.

If you want to know more about this project, access the full report, or how this technology can be applied in other contexts, get in touch. This is just the beginning of a path that I believe has much more to offer!


Deoclécio Paiva de Castro

Ph.D student in Economics
M.Sc in Mathematical Optimization Modeling and Quantitative Methods
B.Sc in Industrial/Production Engineer

príncipe encastelado

The Castle-Bound Prince Syndrome: The Isolation of Power and the Distortion of Reality

In organizations and institutions around the world, both public and private, there exists an invisible and powerful dynamic that isolates leaders from the everyday realities they should be governing or managing. This phenomenon, which I call the Castle-Bound Prince Syndrome, refers to the inevitable disconnection between decision-makers and the true circumstances surrounding their actions. Just like medieval princes locked away in castles, modern leaders often find themselves isolated by a layer of intermediaries—”towers” that filter, adjust, or even distort information before it reaches the “prince” at the top.

The Castle-Bound Prince Syndrome has its roots in something very human: the way power and organizational hierarchy function. At the top of the structure, leaders often rely on their subordinates, advisors, and managers to obtain information that will shape their decisions. However, these intermediaries, in turn, also have their own agendas, fears, and interests. They may distort the reality they present, either out of fear of negative repercussions or to protect their own positions. The result is that the leader—the “prince”—begins to act based on a partial, filtered, and often completely disconnected view of what is truly happening.

The Origin of the Concept

The Castle-Bound Prince Syndrome is not a concept that emerged theoretically or academically. It was born from practical observation and direct experience I had over the years while working in strategic positions, especially in the public sector. The first time I noticed this dynamic was during my involvement in managing social media and political campaigns in the early 2010s, when I made my first notes about this idea. Engaged behind the scenes of politics, I closely witnessed how leaders—often well-intentioned—made crucial decisions based on information that, although it passed through various layers of analysis and filters, often did not reflect the complete reality. These leaders, like princes isolated in castles, became hostages to their towers of information, without direct access to what was truly happening in the “kingdom.”

Over time, I began to understand that this syndrome was common, not only in the public sector but also in large corporations and other hierarchical organizations. The gap between operational reality and decisions made at the top was a structural problem. What began as a specific observation transformed, for me, into a key concept for understanding how many institutions fail to reach their true potential.

The Impact of the Syndrome

The Castle-Bound Prince Syndrome has profound consequences. A leader isolated from reality tends to make decisions based on incomplete or distorted information, which in turn leads to errors in judgment that can be costly for the organization. In the public sector, this can mean misdirected policies that fail to meet the real needs of the population. In the private sector, it means business strategies misaligned with market realities, leading to financial losses and stagnation.

The perpetuation of this syndrome creates a cycle of disconnection and stagnation. The further the leader is from reality, the more difficult it becomes for him or her to correct the course of action and make well-founded decisions. Moreover, the intermediaries—the “towers”—have incentives to maintain this informational barrier, either to protect their positions or to avoid conflicts.

Connection with the “Circle of Mediocrity”

Here, an important connection arises with another concept I developed over the years: the Circle of Mediocrity. The Circle of Mediocrity describes a vicious cycle where mediocre practices are perpetuated within an organization. In many institutions, especially in the public and educational sectors, everyone pretends to fulfill their roles—teachers pretend to teach, students pretend to learn, managers pretend to supervise—while actual performance is far from ideal.

The Castle-Bound Prince Syndrome and the Circle of Mediocrity are intrinsically linked. When a leader is isolated from reality, he or she becomes unable to perceive the mediocrity permeating the organization. This isolation creates the perfect conditions for the Circle of Mediocrity to thrive, as there is no real or effective supervision to break this cycle. The leader, shielded by the organization’s “towers,” continues to believe everything is functioning properly, while mediocrity keeps perpetuating at the lower levels of the structure.

This cycle reinforces the leader’s disconnection, which in turn perpetuates mediocrity. To break this cycle, it’s necessary not only to correct the lack of informational transparency but also to challenge the mediocre practices ingrained in the organizational culture. You can see more about the Circle of Mediocrity here: LINK.

References and Academic Influences

The concept of the Castle-Bound Prince Syndrome did not emerge from a vacuum. It is directly related to various academic theories that address power, information, and governance. Among these influences, the work of Michel Foucault stands out, particularly his theory of the microphysics of power. Foucault argues that power is exercised not only in a centralized manner but through networks of control and surveillance at all levels of society. The Castle-Bound Prince Syndrome reflects this idea by showing how control over the flow of information in an organization, and the power it grants, contributes to the isolation and distortion of reality perceived by leaders.

Additionally, concepts from Agency Theory (Jensen and Meckling, 1976) are fundamental to understanding this syndrome. The asymmetric information between the principal (the leader) and the agents (subordinates) plays a crucial role in distorting the information that reaches the top of the hierarchy. This results in suboptimal decisions and reinforces the leader’s isolation—a classic problem in environments where agents have incentives to conceal the truth or manipulate data.

Herbert Simon’s (1955) theory of bounded rationality also contributes to the understanding of the Castle-Bound Prince Syndrome. Simon argues that decision-makers have a limited capacity to process information and therefore tend to settle for “good enough” solutions rather than seeking the ideal solution. In practice, this means that isolated leaders, upon receiving limited and filtered information, make suboptimal decisions since their options are restricted by the partial and distorted information presented to them.

Game Theory and Conflict Analysis

Another relevant aspect of the Castle-Bound Prince Syndrome can be explored through Game Theory, developed by John von Neumann and Oskar Morgenstern. Game Theory provides a mathematical framework to analyze situations where multiple agents interact strategically. In the context of the syndrome, the leader (or prince) and his subordinates are engaged in a kind of strategic “game,” in which each agent (intermediaries, advisors, and subordinates) has their own incentives and strategies.

Within this “game,” a Nash equilibrium can be reached when no agent has an incentive to unilaterally change their strategy, which often results in a suboptimal state for the organization as a whole. Subordinates, concerned with their own positions, have incentives to filter the information that ascends the hierarchy, protecting their interests and avoiding conflicts with their superiors. The leader, in turn, may believe they are receiving adequate information, and their decisions reflect this suboptimal equilibrium state.

Game Theory also helps analyze conflicts of interest between hierarchical levels. For example, intermediaries have incentives to distort reality to avoid personal risks, while the leader, unaware of the game being played, makes decisions based on a false perception of the situation. This scenario exemplifies a “game of incomplete information,” where the leader does not have full access to the strategies and motivations of subordinates, resulting in decisions that perpetuate disconnection.

Academic Interest and Future Exploration

As PhD student in Economics, I realize that the Castle-Bound Prince Syndrome represents a rich opportunity to model behaviors in institutional contexts. Understanding how information degrades as it moves up the hierarchy and how decisions can be distorted by this disconnection is fundamental for developing more robust economic models. My academic interest lies in exploring how these processes can be mathematically represented and quantified to allow a deeper analysis of the syndrome’s impacts on organizational efficiency. This is an area I intend to explore in more detail in my future research, seeking to integrate aspects of decision theory, asymmetric information, organizational incentives, and Game Theory. The idea is to understand how interactions between leaders and subordinates, mediated by distorted information flows, can be formalized in economic and mathematical models that reflect the reality observed in various institutions. These models can help predict how suboptimal decisions are made and develop strategies to mitigate the impacts of the Castle-Bound Prince Syndrome.

Possible Solutions

Although the Castle-Bound Prince Syndrome is a common phenomenon in hierarchical structures, it is not irreversible. There are strategies that can be implemented to break this isolation and ensure that leaders have access to more accurate and reliable information.

  1. Direct Feedback Channels: One of the first steps to mitigate the syndrome is to create direct communication channels between the leader and the operational layers of the organization. This can be done through independent audits, regular consultations with employees, and the introduction of impartial feedback mechanisms. In Game Theory, this would be equivalent to altering the game’s conditions to increase transparency between parties, reducing the incentives for subordinates to distort reality.
  2. Culture of Transparency: Instituting a culture of organizational transparency is fundamental. When truth is encouraged, intermediaries have less incentive to distort information. Promoting practices that reward honesty and punish information manipulation helps restructure the “game” so that agents’ interests align with those of the organization as a whole.
  3. Active Participation of the Leader: Leaders must be willing to engage directly with the operational levels of the organization, breaking the barrier of the “towers.” By getting closer to daily operations, leaders can develop a clearer and more accurate view of the realities around them. Reducing the distance between hierarchical levels can decrease informational asymmetry, which is essential in both Agency Theory and Game Theory to lessen the impact of incomplete or distorted information.

Final Considerations

The Castle-Bound Prince Syndrome is a powerful metaphor for understanding the dynamics of disconnection between leaders and reality. Just as princes were isolated in their castles in the past, today many leaders, surrounded by layers of intermediaries, make decisions based on incomplete or distorted information. This phenomenon, when unrecognized, can lead to the perpetuation of mediocre practices and organizational stagnation. Recognizing the syndrome is the first step to breaking it. And by doing so, we can pave the way for more effective, more connected, and more responsible leadership.

The integration of Game Theory and classical Decision Theory approaches offers a powerful tool to understand the strategic behavior of agents within the organization and how this affects the quality of decisions made at the top. Understanding the strategic interactions between leaders and subordinates can help develop solutions that better align incentives, creating an organizational dynamic that minimizes the syndrome’s impacts and promotes more honest and effective communication across all hierarchical levels.


References

  • Foucault, M. (1975). Surveiller et punir: Naissance de la prison. Paris: Gallimard.
  • Jensen, M. C., & Meckling, W. H. (1976). Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. Journal of Financial Economics.
  • Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
  • Tversky, A., & Kahneman, D. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica.
  • Simon, H. A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99-118.
  • Von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press.

Deoclécio Paiva de Castro

Ph.D student in Economics
M.Sc in Mathematical Optimization Modeling and Quantitative Methods
B.Sc in Industrial/Production Engineer

circulo da mediocridade

The Circle of Mediocrity: How Conformism and Superficiality Are Weakening Institutions and Society

In recent years, I’ve been observing a phenomenon that, although subtle, has had profound consequences across various spheres of our society: the Circle of Mediocrity. This concept, which I developed throughout my professional experience with digital communication and politics, describes a vicious cycle of stagnation permeating institutions, companies, and society as a whole. It’s a dynamic where mediocrity is not only tolerated but encouraged and perpetuated by systems that inherently resist innovation and competence.

The Circle of Mediocrity forms when mediocre behaviors, ideas, and structures are maintained through negative incentives like conformism, self-preservation, and fear of change. Initiatives that could break this stagnation are often seen as threats to the status quo, and those who advocate for them end up marginalized or silenced. As I often say, “those who excel don’t become inspirations; they become targets,” a phrase that illustrates the suppression of individuals seeking to break this cycle. The cycle perpetuates itself, and institutions, instead of striving for excellence, deepen into a spiral of superficiality and inefficiency.

Superficiality in Politics and the Rise of Spectacle

One of the clearest examples of the Circle of Mediocrity is found in contemporary politics. A few years ago, especially with the advent of social networks, it was expected that these digital platforms would revolutionize how governments interact with their citizens. The promise was that networks would open space for more democratic, transparent, and proactive participation. However, what we witnessed was the opposite phenomenon: politics transformed into a spectacle, with candidates more concerned about garnering likes and views than presenting concrete solutions to societal problems.

This transformation of politics into spectacle resembles Guy Debord’s concept in his work The Society of the Spectacle, which warns about the transformation of social relations into mere representations. Politics, much like entertainment, becomes a performance where content is secondary. This shift from essence to appearance feeds the Circle of Mediocritybecause it reduces the quality of debates and proposals in favor of slogans and images that go viral.

Political campaigns have become crude and empty, filled with populist slogans and personal attacks instead of serious and profound debates. Superficiality has taken over the electoral scene, and candidates seem more like they’re competing for entertainment slots than for positions of leadership and responsibility. The consequence is that elected officials, instead of proposing transformative public policies, continue spinning in the Circle of Mediocrity, conducting their mandates reactively, without real leadership or long-term vision.

Education: The Factory of Stagnation

The Circle of Mediocrity is also evident in the educational system. From teachers who pretend to teach, to students who pretend to learn, to administrators who pretend to supervise, the cycle of mediocrity establishes itself in all spheres of education. Educational institutions are often more concerned with maintaining an appearance of normality and continuity than promoting structural changes that genuinely improve the quality of education.

This dynamic results in an uneducated society lacking critical capacity, where superficiality also expands into the digital realm. The youth, who should be protagonists of the future, find themselves in a system that values innovation and creativity very little. Instead of being prepared to face the challenges of a constantly changing world, they are molded to maintain the status quo, perpetuating a cycle of stagnation.

Here, it’s important to recall Foucault’s concepts about power and the “micro-powers” that sustain the system. Power, in educational institutions, is exercised subtly and pervasively, where established norms and regulations prevent any innovation that could break the cycle of mediocrity. Teachers who try to innovate are often silenced by control structures, maintaining the cycle of stagnation.

Corporate Environment: Flattery and Conformism on the Rise

The corporate world is not immune to the Circle of Mediocrity. Within many organizations, what should be a space for innovation, collaboration, and delivering concrete results turns into a stage for internal politics, where flattery is more valued than competence. Professionals who seek to stand out by delivering results are often overlooked in favor of those who know how to “navigate” the company’s internal political dynamics, avoiding conflicts and staying aligned with the superiors’ discourse.

This environment creates a paralyzing effect on companies, which, instead of innovating and seeking excellence, prefer to maintain the status quo. Ineffective processes continue to be perpetuated because challenging this structure would require courage—something the Circle of Mediocrity does not encourage. As a result, organizations lose competitiveness and stagnate, trapped in a culture of conformism that hinders growth and evolution.

The Emergence of the Circle of Mediocrity Concept

The concept of the Circle of Mediocrity emerged from my own observations over the years, especially in my work with digital communication and politics. In 2001, I began my first contacts with the job market and the political environment. By 2010, with more maturity, I actively participated in the campaign that propelled Cid Gomes’ candidacy for governor of Ceará. Later, as a professional within the government structure, I realized that something bigger was at play within institutions and power structures. The potential we saw in social networks and new forms of communication to transform public debate was often underestimated or, worse, viewed with suspicion by those who preferred to maintain the old ways of doing politics. In 2011, when I was responsible for creating the social networks for the Government of Ceará—encouraged by the Governor himself, something completely innovative in Brazil at the time—I heard more than once the phrase: “The internet, this social media thing, will never be relevant to politics.” Here, it’s important to acknowledge Cid Gomes’ ever-active propensity for proactivity, technology, and excellence—something that, while inspiring many, was seen by others as a threat to the status quo. It was then that the idea of the “castle-bound prince” syndrome emerged for me, but that’s a topic for another post.

My experience in government and many electoral campaigns showed me that attempts to innovate and break away from superficiality were often met with resistance. The Circle of Mediocrity became evident in every sphere we touched, and the idea that mediocrity was not only accepted but reinforced began to take shape. This cycle repeats in so many areas that it became impossible to ignore. The more I reflected on this, the more the concept solidified as a clear explanation for the stagnation I observed around me.

Mechanisms That Feed Mediocrity

The perpetuation of the Circle of Mediocrity is rooted in various social, psychological, and institutional mechanisms. One example is the Dunning-Kruger effect, a psychological phenomenon that explains how people with less competence tend to overestimate their abilities. This leads them to reject any form of innovation or change that could expose their limitations. In environments where these people occupy positions of power, the cycle of mediocrity perpetuates because changes that could improve the system are systematically avoided.

Additionally, there’s the phenomenon of institutional inertia, where bureaucracies and power structures continue to function the same way even when they’re clearly ineffective. Governments, companies, and organizations resist innovation because the process of change is seen as dangerous or disruptive to the balance of power relations. In this scenario, conformism and self-preservation prevail over competence and innovation. Foucault also helps us understand this dynamic through his concept of micro-powers: control doesn’t manifest only in grand actions or decisions but in how the everyday details of an organization control behavior and prevent disruptions.

The Circle of Mediocrity as Dynamic Conflict: Game Theory and Prospect Theory

The Circle of Mediocrity can also be understood through the lens of Game Theory and Prospect Theory. In Game Theory, the cycle is sustained as a dynamic conflict where the agents involved (leaders, professionals, institutions) make rational choices that perpetuate the status quo, even if these choices aren’t the most efficient. Negative cooperation, in this case, arises when all involved prefer to maintain a mediocre level of performance since changing the system implies risks and costs that no one wants to assume.

This dynamic can be analyzed based on concepts of equilibria with limited horizons, such as General Meta-Rationality (GMR)Symmetric Meta-Rationality (SMR), and Sequential Stability (SEQ). These concepts explain how, in conflict situations, agents may opt for decisions that, while rational for themselves, result in suboptimal solutions for the group as a whole. Often, the agent prefers to maintain the status quo, avoiding a globally better decision out of fear of individual consequences like punishments or suppression by opponents. In Sequential Equilibrium, for example, players base their decisions on expectations about the future behavior of their opponents and possible reactions to their actions. This means that even when aware of better available options, individuals and institutions hesitate to adopt them, perpetuating the Circle of Mediocrity. The central idea is that the decision to maintain stability, even if suboptimal, becomes a rational response in the face of fear of individual losses, which in turn creates a collective inertia that prevents innovation and keeps the system trapped in an unfavorable equilibrium for all.

Prospect Theory, by Kahneman and Tversky, helps us understand why people prefer to maintain the status quo even when change would be advantageous. The theory teaches us that individuals tend to value potential losses more than future gains, leading to risk aversion. In the context of the Circle of Mediocrity, this means agents prefer to cling to what they already know, even if it’s ineffective, rather than risk an innovation that could offer better results but also brings uncertainties.

These strategic choices, grounded in risk aversion and a tacit cooperation to maintain the cycle, feed the continuity of mediocrity. Thus, the Circle of Mediocrity can be seen as a suboptimal solution to a dynamic game where everyone loses in the long run, but no one wants to risk disruption in the short term.

The Relationship with the “Castle-Bound Prince Syndrome

The Circle of Mediocrity is closely linked to the Castle-Bound Prince Syndrome, a concept I developed to describe how leaders, isolated in their positions of power, end up receiving filtered and distorted information from advisors and intermediaries. This distance from reality creates a biased perspective on what’s truly happening “on the ground,” resulting in decisions based on incomplete or manipulated information. This perpetuates stagnation since the leader believes everything is under control or functioning as expected when, in reality, the cycle of mediocrity is active and growing.

When leaders are disconnected from reality due to this “syndrome,” they become incapable of recognizing the need to break the Circle of Mediocrity. Instead of seeking real solutions based on concrete data and innovation, they continue reinforcing the same practices that keep mediocrity at the center of decisions. For a more detailed analysis of how the Castle-Bound Prince Syndrome affects leadership and decision-making, check out my dedicated post on this topic [link to the post].

How to Break the Circle of Mediocrity

Breaking the Circle of Mediocrity is a task that requires courage and a willingness to challenge the status quo. First, it’s necessary to recognize that mediocrity is not inevitable. Institutions, organizations, and individuals can choose a different path—a path that values innovation, merit, and competence.

For this to happen, Game Theory shows us that incentives need to change so that all players involved in the system have something to gain by abandoning mediocrity. Negative cooperation, where agents prefer to maintain the status quo to avoid risks, needs to be replaced by incentives that reward those who challenge the cycle. This means creating a culture that values excellence, where innovation and the delivery of results are significantly rewarded.

Prospect Theory by Kahneman and Tversky teaches us that one of the most effective ways to overcome the risk aversion that maintains the cycle is to restructure rewards and consequences. By emphasizing the benefits of positive change instead of focusing on possible risks, we can create a new dynamic where innovation becomes more attractive than maintaining the vicious cycle.

Creating a culture that values excellence is fundamental. This involves changing incentives, both in the public and private sectors, so that people who stand out by delivering results and showcasing creativity are rewarded rather than marginalized. It’s also essential to invest in critical and digital education, empowering the next generations to be active agents of change and innovation.

Institutions need to make room for new ideas to flourish, even if those ideas challenge the status quo. Structures that reward conformism and superficiality must be questioned and reformed, with a real focus on transformation rather than merely maintaining the system.As instituições precisam abrir espaço para que novas ideias floresçam, mesmo que essas ideias desafiem o status quo. As estruturas que premiam o conformismo e a superficialidade devem ser questionadas e reformadas, com um foco real na transformação, e não na mera manutenção do sistema.

Conclusion

The Circle of Mediocrity is a real and dangerous phenomenon affecting various areas of contemporary society. It perpetuates stagnation and prevents institutions and individuals from reaching their full potential. However, this vicious cycle can be broken. To do so, it’s necessary to value competence, innovation, and excellence, creating spaces where mediocrity has no place. Transformation begins with awareness—and the courage to challenge the status quo. Recognizing that mediocrity is often protected by those who benefit from it is the first step toward making real changes. Only by exposing this dynamic and promoting a culture of excellence can we hope for our institutions, companies, and society as a whole to move toward a more innovative and efficient future.

Reflecting on the Circle of Mediocrity is not just a critique of the present but an invitation to rethink how our structures operate and how we can, together, break this cycle. If we want a more just, productive, and critical society, it’s essential to abandon conformism and mediocrity, and that starts with each of us—in our daily decisions and actions.


References

  • Debord, G. (1967). La Société du Spectacle. Paris: Buchet-Chastel.
  • Foucault, M. (1975). Surveiller et punir: Naissance de la prison. Paris: Gallimard.
  • Kahneman, D. (2011). Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
  • Tversky, A., & Kahneman, D. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
  • Von Neumann, J., & Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton University Press.
  • Dunning, D., & Kruger, J. (1999). Unskilled and Unaware of It: How Difficulties in Recognizing One’s Own Incompetence Lead to Inflated Self-Assessments. Journal of Personality and Social Psychology, 77(6), 1121-1134.
  • Simon, H. A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99-118.

Deoclécio Paiva de Castro

Ph.D student in Economics
M.Sc in Mathematical Optimization Modeling and Quantitative Methods
B.Sc in Industrial/Production Engineer

GMCR authors graphs

Graph Model for Conflict Resolution

The Graph Model for Conflict Resolution (GMCR) is a powerful mathematical tool used to analyze and resolve complex conflicts across various fields, including negotiations, environmental management, and international disputes. Developed in the 1980s, the model has evolved over time, incorporating new techniques and approaches to handle uncertainty and multiple preferences. In this post, you will find a comprehensive and detailed bibliography on GMCR, featuring references from the earliest publications to the most recent ones, organized chronologically for easy reference.

Bibliographic history

Until 1980

  1. Fraser, N. M., & Hipel, K. W. (1979). “Solving complex conflicts.” IEEE Transactions on Systems, Man, and Cybernetics, 9(12), 805-816.
  2. Fraser, N. M., & Hipel, K. W. (1980). “Metagame analysis of the Poplar River conflict.” Journal of the Operational Research Society, 31(5), 377-385.

1981 – 1990

  1. Fraser, N. M., & Hipel, K. W. (1984). Conflict Analysis: Models and Resolutions. North-Holland, New York.
  2. Kilgour, D. M., Hipel, K. W., & Fang, L. (1987). “The Graph Model for Conflicts.” Automatica, 23(1), 41-55.
  3. Fang, L., Hipel, K. W., & Kilgour, D. M. (1989). Interactive Decision Making: The Graph Model for Conflict Resolution. Wiley.

1991 – 2000

  1. Hipel, K. W., Kilgour, D. M., & Fang, L. (1993). “The Graph Model for Conflicts.” Automatica, 29(5), 1425-1437.
  2. Kilgour, D. M., Hipel, K. W., & Fang, L. (1995). “The Graph Model for Conflict Resolution: Past and Future.” Interfaces, 25(6), 114-133.
  3. Hipel, K. W., Fang, L., & Kilgour, D. M. (1997). “The Decision Support System GMCR II in Environmental Conflict Management.” Applied Mathematics and Computation, 83(2-3), 117-152.
  4. Kilgour, D. M., Hipel, K. W., Fang, L., & Peng, X. (1998). “Applying the decision support system GMCR II to peace operations.” In Analysis for and of the Resolution of Conflict. Canadian Peacekeeping Press.

2001 – 2010

  1. Kilgour, D. M., Fang, L., & Hipel, K. W. (2001). Negotiation in Environmental Conflicts: The Graph Model Approach. Springer.
  2. Hipel, K. W., Kilgour, D. M., Fang, L., & Peng, X. (2001). “Strategic decision support for the services industry.” IEEE Transactions on Engineering Management, 48(3), 358-369.
  3. Li, K. W., Hipel, K. W., Kilgour, D. M., & Fang, L. (2004). “Preference uncertainty in the graph model for conflict resolution.” IEEE Transactions on Systems, Man, and Cybernetics, Part A, 34(4), 507-520.
  4. Li, K. W., Kilgour, D. M., & Hipel, K. W. (2005). “Status quo analysis in the graph model for conflict resolution.” Journal of the Operational Research Society, 56(6), 699-707.
  5. Obeidi, A., Hipel, K. W., & Kilgour, D. M. (2005). “Perception and emotion in the graph model for conflict resolution.” Proceedings of the 2005 IEEE International Conference on Systems, Man, and Cybernetics.
  6. Obeidi, A., Kilgour, D. M., & Hipel, K. W. (2009). “Perceptual stability analysis of a graph model for conflict resolution.” Group Decision and Negotiation, 18(3), 261-277.
  7. Peng, X., Hipel, K. W., Kilgour, D. M., & Fang, L. (2008). “A Graph Model for Conflict Resolution with Recursive Preferences.” Group Decision and Negotiation, 17(6), 491-513.

2011 – 2024

  1. He, S., Kilgour, D. M., & Hipel, K. W. (2017). “Analyzing market competition between Airbus and Boeing using a duo hierarchical model for conflict resolution.” Journal of Systems Science and Systems Engineering, 26(6), 683-710.
  2. Xu, H., Hipel, K. W., Kilgour, D. M., & Fang, L. (2015). “A Multiple Participant–Multiple Criteria Decision-Making Model Based on the Graph Model for Conflict Resolution.” Journal of Systems Science and Systems Engineering, 24(2), 188-210.
  3. Fang, L., Hipel, K. W., Kilgour, D. M., & Peng, X. (2013). “A Decision Support System for Interactive Decision Making, Part II: Analysis and Application.” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(2), 185-197.
  4. Li, Y., Hipel, K. W., Kilgour, D. M., & Fang, L. (2016). “Preference Elicitation in the Graph Model for Conflict Resolution.” European Journal of Operational Research, 252(1), 352-363.
  5. Fang, L., Hipel, K. W., Kilgour, D. M., & Wang, L. (2020). “Uncertainty Modeling in the Graph Model for Conflict Resolution.” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(1), 82-92.
  6. Wang, L., Hipel, K. W., Kilgour, D. M., & Fang, L. (2021). “Strategic Analysis of Conflicts with Incomplete Information Using the Graph Model.” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(5), 2940-2952.
  7. Xu, H., Kilgour, D. M., & Hipel, K. W. (2018). “An Algorithm for Multiple Criteria Status Quo Analysis in the Graph Model for Conflict Resolution.” European Journal of Operational Research, 271(2), 720-731.
  8. Fang, L., Hipel, K. W., & Kilgour, D. M. (2019). “Analysis of Multilateral Conflicts with Strategic Uncertainty.” Group Decision and Negotiation, 28(3), 573-591.
  9. Sabino, E. R., & Rêgo, L. C. (2024). “Minimax regret stability in the graph model for conflict resolution.” European Journal of Operational Research, 314(3), 1087-1097.

Deoclécio Paiva de Castro

Ph.D student in Economics
M.Sc in Mathematical Optimization Modeling and Quantitative Methods
B.Sc in Industrial/Production Engineer