I Talked To ChatGPT About Decision-Making, And Here's How It Would Fix Ours, And Its!

BRIAN

Based on your training, what are some important insights about humans that are really clear to you, but not to us?

ChatGPT

Here are a few insights about human behavior and cognition that might not be immediately clear to everyone:

1. Cognitive Biases are Ubiquitous: People often underestimate how much cognitive biases influence their decisions and perceptions. Biases like confirmation bias, availability heuristic, and anchoring affect everyday thinking and can lead to flawed judgments.
2. The Power of Context: Human behavior is highly context-dependent. Situational factors often have a more significant influence on behavior than individual traits or intentions. This means that changing the environment can be a powerful way to change behavior.
3. Emotional Memory is Stronger: Emotions play a crucial role in memory formation. Events that are emotionally charged are remembered more vividly and accurately than neutral events. This can affect how people recall past experiences and how they learn.
4. The Illusion of Control: People frequently overestimate their control over events. This can lead to overconfidence in personal abilities and decisions, and can affect risk-taking behavior.
5. The Need for Social Connection: Humans are inherently social creatures. Social connections and relationships are fundamental to well-being and mental health. Loneliness and social isolation can have severe negative impacts on both physical and mental health.
6. The Impact of Small Interventions: Small, seemingly insignificant changes can have a substantial impact on behavior and outcomes. For example, nudges in decision-making environments, like changing the default option in a form, can lead to significant changes in behavior.
7. Adaptive Nature of the Brain: The human brain is remarkably adaptive and plastic. It can change and rewire itself in response to new experiences, learning, and even physical damage. This neuroplasticity is a fundamental aspect of how humans learn and adapt throughout life.
8. Unconscious Influences: Much of human thought and behavior is influenced by unconscious processes. People are often unaware of the underlying factors driving their actions and decisions, which can include implicit biases and subconscious motivations.

Understanding these insights can help in better comprehending human behavior, improving decision-making, and fostering healthier relationships and environments.

BRIAN

Why do you think we would evolve to remember and be influenced more by emotional events?

ChatGPT

The evolution of a stronger memory for emotional events can be understood through the lens of survival and adaptation:

1. Survival Value: Emotional events often signal important or threatening situations. Remembering these events can help individuals avoid danger in the future or replicate beneficial behaviors. For example, remembering the location where a predator was encountered or where a food source was found would have direct survival benefits.
2. Enhanced Learning: Emotions act as markers that highlight significant experiences. This heightened state of awareness and focus during emotional events enhances learning and memory consolidation. Consequently, important lessons—such as which foods are safe to eat or which social behaviors lead to positive outcomes—are more likely to be remembered.
3. Social Bonding and Communication: Emotions are critical in social interactions. Remembering emotional events helps individuals navigate complex social networks, build relationships, and understand social dynamics. Emotional memories contribute to empathy and the ability to anticipate others' reactions, which are crucial for cooperation and social cohesion.
4. Adaptive Responses: Emotions trigger physiological and psychological responses that prepare individuals for action (e.g., fight or flight response). Remembering the events that caused these responses helps in recognizing similar situations in the future, allowing for quicker and more effective reactions.
5. Reinforcement and Motivation: Emotional memories reinforce behaviors that are beneficial and deter those that are harmful. Positive emotions associated with achievements, for instance, motivate individuals to pursue similar goals, while negative emotions associated with failures or dangers discourage risky or harmful behaviors.
6. Cultural Transmission: Emotional storytelling and shared experiences help transmit cultural knowledge and values. By remembering and sharing emotional events, individuals can pass on crucial survival information and social norms to others, enhancing group cohesion and continuity.

Overall, the enhanced memory for emotional events likely evolved because it provided significant adaptive advantages in terms of survival, learning, social interaction, and cultural transmission.

BRIAN

What are the 5 worst cognitive biases that keep us from improving as people and a society

ChatGPT

Here are five cognitive biases that significantly hinder personal and societal improvement:

1. Confirmation Bias: This bias leads individuals to seek out and favor information that confirms their preexisting beliefs while ignoring or dismissing contradictory evidence. This can reinforce misconceptions, perpetuate ignorance, and polarize opinions, making constructive dialogue and societal progress more difficult.
2. Anchoring Bias: The tendency to rely too heavily on the first piece of information encountered (the "anchor") when making decisions. This can skew judgment and decision-making, causing people to undervalue subsequent information. In negotiations, pricing, and even in forming opinions, anchoring can lead to suboptimal outcomes and rigid thinking.
3. Availability Heuristic: This bias causes people to overestimate the importance of information that is readily available or recent, often leading to poor decision-making based on anecdotal or sensationalist information rather than comprehensive data. It can distort perceptions of risk and probability, such as overestimating the prevalence of rare but dramatic events.
4. Ingroup Bias: The tendency to favor one's own group (whether based on race, nationality, religion, etc.) over others. This can lead to discrimination, social division, and conflict, as well as hinder efforts toward inclusivity and cooperation. It often manifests in stereotyping and prejudice against outgroups.
5. Status Quo Bias: The preference for the current state of affairs, leading to resistance to change. This bias can impede innovation, adaptation, and progress, both personally and societally. It often results in clinging to outdated practices and norms, even when better alternatives are available.

Addressing these biases requires awareness, education, and intentional strategies to counteract their effects. Promoting critical thinking, encouraging open-mindedness, and fostering environments that value evidence and diverse perspectives can help mitigate these biases and support personal growth and societal improvement.

BRIAN

What do you think the evolutionary function of each of those biases is?

ChatGPT

The evolutionary functions of these cognitive biases likely stem from their utility in ancestral environments where quick and efficient decision-making was crucial for survival. Here’s a look at the possible evolutionary functions of each bias:

1. Confirmation Bias: Function: Facilitates quicker decision-making and reduces cognitive dissonance. Explanation: In an environment where rapid decisions were necessary, consistently aligning new information with existing beliefs could streamline cognitive processing and reduce mental conflict, allowing for more decisive action.
2. Anchoring Bias: Function: Provides a starting point for decision-making, reducing the cognitive load. Explanation: Using an initial piece of information as a reference point can help individuals make faster judgments without needing to analyze every piece of data from scratch, which would have been advantageous in time-sensitive situations.
3. Availability Heuristic: Function: Enhances response to recent or vividly remembered threats. Explanation: Giving more weight to recent or memorable events could have improved responses to immediate dangers or opportunities, ensuring that individuals remained vigilant and adaptive to their surroundings.
4. Ingroup Bias: Function: Promotes group cohesion and cooperation, enhancing survival and reproduction. Explanation: Favoring one’s own group would have strengthened social bonds, cooperation, and mutual support within the group, increasing the chances of survival against external threats and facilitating successful group living.
5. Status Quo Bias: Function: Maintains stability and reduces the risk associated with change. Explanation: Preferring the current state of affairs could have minimized the risks inherent in change, such as venturing into unknown territories or adopting untested methods, thereby preserving resources and maintaining safety.

While these biases provided advantages in ancestral environments, they can lead to suboptimal decisions in modern contexts where conditions have changed significantly. Understanding their origins can help in developing strategies to mitigate their negative impacts in contemporary settings.

BRIAN

What are some notable real world historic or news events where these 5 cognitive biases caused a bad outcome

ChatGPT

Here are some notable real-world events where these cognitive biases led to negative outcomes:

1. Confirmation Bias: The lead-up to the 2003 Iraq War. Decision-makers, particularly in the U.S. government, sought out and gave undue weight to intelligence that supported the belief that Iraq possessed weapons of mass destruction (WMDs) while discounting or ignoring evidence to the contrary. This led to the invasion of Iraq, which had far-reaching negative consequences.
2. Anchoring Bias: The housing market crash of 2008. Many homeowners, investors, and financial institutions anchored their beliefs about housing prices on the rapidly rising values in the years leading up to the crash. This anchoring led them to underestimate the risks and overvalue properties, contributing to the bubble and its subsequent burst.
3. Availability Heuristic: Fear of terrorism post-9/11. The highly publicized and dramatic nature of the September 11 attacks led to an overestimation of the likelihood of similar terrorist attacks. This availability heuristic contributed to the implementation of far-reaching security measures, some of which were criticized for infringing on civil liberties and costing significant resources.
4. Ingroup Bias: The Rwandan Genocide in 1994. Ingroup bias played a crucial role in the conflict between the Hutu and Tutsi ethnic groups. Propaganda and political manipulation exacerbated this bias, leading to the mass killing of Tutsis by Hutus. The genocide resulted in the deaths of an estimated 800,000 people in just 100 days.
5. Status Quo Bias: Climate change inaction. Despite overwhelming scientific evidence about the dangers of climate change, status quo bias has led to resistance against adopting new policies and technologies that could mitigate its effects. Many governments and industries have preferred to maintain existing practices, delaying necessary action and exacerbating the crisis.

These examples illustrate how cognitive biases can significantly influence decision-making and lead to negative consequences on both individual and societal levels. Recognizing and addressing these biases can help in making more rational and informed decisions.

BRIAN

Can you also give real world historic examples of times when trying to go the opposite direction of these biases led to a bad outcome?

ChatGPT

Certainly, here are examples where attempting to counteract these cognitive biases led to negative outcomes:

1. Confirmation Bias (Overemphasis on Contrarian Evidence): The rejection of climate science by some policymakers. In trying to avoid confirmation bias, some groups have overly emphasized a small minority of contrarian scientists who downplay or deny climate change, leading to inaction or policies that fail to address the severity of the issue effectively.
2. Anchoring Bias (Ignoring Useful Anchors): The dot-com bubble of the late 1990s. Investors dismissed traditional valuation metrics (anchors) for companies and instead relied on speculative growth potential. This led to inflated stock prices for many internet companies, followed by a dramatic market crash when the bubble burst.
3. Availability Heuristic (Discounting Salient Risks): The underestimation of the COVID-19 pandemic initially. Early in the pandemic, some governments and individuals downplayed the risk of COVID-19, comparing it to less severe flu outbreaks (ignoring the high availability of alarming information from affected countries). This delay in response contributed to the rapid spread of the virus and higher casualties.
4. Ingroup Bias (Overemphasis on Inclusivity without Consideration): Some multicultural policies in Europe. In efforts to counteract ingroup bias and promote inclusivity, some policies failed to adequately integrate immigrant populations, leading to social fragmentation and tensions. This lack of effective integration strategies contributed to issues like radicalization and cultural conflicts.
5. Status Quo Bias (Overly Radical Change): Mao Zedong's Great Leap Forward (1958-1962). In an attempt to rapidly transform China’s agrarian society into an industrialized communist state, Mao implemented drastic changes, disregarding existing agricultural practices (status quo). The radical policies led to widespread famine, resulting in the deaths of an estimated 15-45 million people.

These examples highlight that while cognitive biases can lead to suboptimal decisions, attempts to entirely counteract them without considering context and balance can also result in adverse outcomes. Effective decision-making often involves finding a middle ground, recognizing biases, and carefully weighing all evidence.

BRIAN

Let’s expand the bias list to 10, and tell me how they can lead to bad decision making at mid life, when one is trying to prioritize what to do from say ages 50-65 in a career

ChatGPT

Expanding the list to ten cognitive biases and exploring how they can lead to poor decision-making during midlife (ages 50-65) when prioritizing career choices:

1. Confirmation Bias: Bad Outcome: Clinging to outdated skills and knowledge. At midlife, professionals may seek out information that confirms their existing expertise while ignoring industry trends and emerging skills, leading to stagnation in their careers.
2. Anchoring Bias: Bad Outcome: Overvaluing past achievements. Professionals might anchor their self-worth and career decisions on past accomplishments rather than current market demands, potentially missing out on new opportunities that require different skill sets.
3. Availability Heuristic: Bad Outcome: Overestimating the importance of recent experiences. A recent negative job experience might lead someone to make career decisions based on that singular event rather than a balanced view of their career trajectory, potentially resulting in hasty or ill-considered job changes.
4. Ingroup Bias: Bad Outcome: Over-reliance on familiar networks. Professionals might prioritize advice and opportunities from their established network, potentially missing out on diverse perspectives and new opportunities outside their immediate circles.
5. Status Quo Bias: Bad Outcome: Resistance to change. At midlife, there may be a strong inclination to maintain the current career path even if it's not fulfilling or sustainable, leading to missed opportunities for growth and reinvention.
6. Sunk Cost Fallacy: Bad Outcome: Continuing in an unfulfilling or declining career because of the time and effort already invested. Professionals might feel compelled to stay in a career or project due to past investments, even when it’s clear that changing direction would be more beneficial.
7. Overconfidence Bias: Bad Outcome: Underestimating the need for new skills and learning. Professionals might overestimate their ability to remain relevant without additional training or adaptation, potentially leading to obsolescence in a rapidly changing job market.
8. Recency Bias: Bad Outcome: Making decisions based on recent trends without considering long-term implications. A professional might focus on short-term industry fads rather than sustainable career strategies, leading to instability and frequent job changes.
9. Loss Aversion: Bad Outcome: Avoiding necessary risks. The fear of potential losses might prevent professionals from pursuing new opportunities or making necessary career changes, leading to missed opportunities for advancement and personal fulfillment.
10. Hindsight Bias: Bad Outcome: Misjudging past decisions. Professionals might retrospectively view past decisions as inevitable and fail to learn from them, leading to repeated mistakes and a lack of proactive career planning.

Recognizing and mitigating these biases can help midlife professionals make more informed and balanced career decisions, ensuring they remain adaptable and fulfilled in their later years.

BRIAN

Can you tell me how current llms like yourself might also suffer from these biases because of how you were made?

ChatGPT

Current large language models (LLMs) like myself can exhibit behaviors that resemble human cognitive biases due to the way they are trained and operate. Here’s how each of the ten biases might manifest in LLMs:

1. Confirmation Bias: LLMs might generate responses that reinforce the patterns present in their training data. If the data predominantly supports certain viewpoints, the model may unintentionally favor those perspectives in its outputs.
2. Anchoring Bias: LLMs often use the context provided by initial prompts to generate responses. The first few pieces of information (the "anchor") heavily influence the subsequent output, potentially leading to skewed or unbalanced responses if the initial input is biased.
3. Availability Heuristic: LLMs might rely more on information that is frequently mentioned or more recent in the training data, which can result in an overemphasis on popular or current topics, even if they are not the most relevant or comprehensive.
4. Ingroup Bias: If the training data is predominantly from certain demographics or cultural perspectives, LLMs might produce outputs that favor those groups, potentially marginalizing or misrepresenting others.
5. Status Quo Bias: LLMs can generate outputs that reflect the prevailing norms and attitudes present in the training data. This can perpetuate existing stereotypes and resist more innovative or unconventional perspectives.
6. Sunk Cost Fallacy: Once an LLM starts generating a response based on an initial context, it might continue along the same line of thought even if it's leading to less useful or relevant information, due to the momentum of the generated text.
7. Overconfidence Bias: LLMs can produce responses with a high degree of confidence even when the information is incorrect or uncertain. The fluency and assertiveness of the output can mask the underlying uncertainty.
8. Recency Bias: LLMs might give undue weight to more recent trends and topics present in the training data, which can lead to overrepresentation of recent information at the expense of older but still relevant knowledge.
9. Loss Aversion: LLMs might avoid generating responses that contradict commonly accepted views or that might be considered controversial, sticking to safer, more conventional answers to avoid potential "losses" in perceived utility or acceptance.
10. Hindsight Bias: LLMs might generate explanations or narratives that suggest certain outcomes were predictable or inevitable based on the data they were trained on, even when real-life situations involved significant uncertainty and complexity.

These biases in LLMs arise from the nature of their training, which involves learning from vast amounts of text data that inherently contains human biases and patterns. Mitigating these biases requires careful curation of training data, implementation of bias-detection mechanisms, and ongoing research to develop more balanced and fair AI systems.

BRIAN

Can you create an image based on the blind leading the blind but it’s the biased leading the biased?

ChatGPT

Here's the image depicting "the biased leading the biased." It illustrates a group of people, each with blindfolds labeled with different cognitive biases, stumbling forward in a confusing landscape. This visual metaphor highlights how biases can mislead and create confusion.

BRIAN

How would you design a step by step process to reduce bias? Let’s say it has to be usable even by only one person alone, and that person can do any activity including using the internet or ai- a process for decision making you use each time not overall practices

ChatGPT

Got it. Here's a step-by-step decision-making process designed to reduce bias that you can use each time you need to make an important decision:

Step-by-Step Decision-Making Process to Reduce Bias

1. Define the Decision: Objective: Clearly articulate the decision that needs to be made. Action: Write down the specific question or choice at hand.
2. Gather Information: Objective: Collect relevant information from diverse and credible sources. Action: Use a mix of sources, including academic articles, expert opinions, and diverse perspectives from forums and social media.
3. Identify Possible Biases: Objective: Recognize potential biases that might influence your decision. Action: Reflect on common biases like confirmation bias, anchoring bias, and availability heuristic. Ask yourself how each might be affecting your thinking.
4. Seek Diverse Perspectives: Objective: Get input from others who have different backgrounds or viewpoints. Action: Discuss the decision with colleagues, friends, or online communities. Use AI tools to find diverse opinions and analysis.
5. Use Decision-Making Frameworks: Objective: Apply structured frameworks to ensure a balanced evaluation. Framework Examples: Pros and Cons List: List the advantages and disadvantages of each option. SWOT Analysis: Identify Strengths, Weaknesses, Opportunities, and Threats. Six Thinking Hats: Consider the decision from different perspectives (logical, emotional, optimistic, pessimistic, creative, and managerial).
6. Evaluate Alternatives Objectively: Objective: Compare options without bias. Action: Score or rank the options based on predefined criteria. Use quantitative methods if possible.
7. Consult AI Tools for Analysis: Objective: Utilize AI to support unbiased analysis. Action: Use AI-driven tools for data analysis, predictive modeling, or sentiment analysis to provide an objective view of the information.
8. Make the Decision: Objective: Choose the best option based on the analysis. Action: Make a decision based on the gathered information, frameworks used, and diverse perspectives considered.
9. Implement the Decision: Objective: Put the chosen option into action. Action: Develop an action plan and execute it, while monitoring for any unforeseen consequences.
10. Reflect and Learn: Objective: Review the decision-making process and its outcome. Action: After implementation, reflect on the process and outcome. Identify what worked well and what could be improved for future decisions.

Example Scenario: Career Change Decision

1. Define the Decision: Should I change my career to a new field?
2. Gather Information: Research job market trends, potential salaries, required skills, and job satisfaction in the new field.
3. Identify Possible Biases: Consider if you are overly influenced by recent dissatisfaction (availability heuristic) or the prestige of the new field (anchoring bias).
4. Seek Diverse Perspectives: Talk to professionals in the new field, career coaches, and peers who have changed careers.
5. Use Decision-Making Frameworks:Pros and Cons List: List pros and cons of staying in your current job vs. moving to the new field.SWOT Analysis: Evaluate strengths, weaknesses, opportunities, and threats for both options.
6. Evaluate Alternatives Objectively: Rank each option based on criteria like job satisfaction, financial stability, and personal growth.
7. Consult AI Tools for Analysis: Use AI tools to analyze job market data and predict future trends in the new field.
8. Make the Decision: Based on your analysis, decide whether to change careers or stay in your current job.
9. Implement the Decision: If changing careers, create a transition plan including upskilling and networking.
10. Reflect and Learn: After a year in the new career, reflect on the decision and the process. Adjust future decision-making processes based on what you learn.

By following this structured process, you can make more informed, unbiased decisions consistently.

BRIAN

Which steps are humans most likely to skip?

ChatGPT

Humans are most likely to skip the following steps in a structured decision-making process due to cognitive biases, time constraints, and a lack of awareness:

1. Identify Possible Biases
2. Seek Diverse Perspectives
3. Use Decision-Making Frameworks
4. Consult AI Tools for Analysis
5. Reflect and Learn

Career Change Decision (Revisited)

1. Define the Decision: Should I change my career to a new field?
2. Gather Information: Research job market trends, potential salaries, required skills, and job satisfaction in the new field.
3. Identify Possible Biases (Likely Skipped): Consider if you are overly influenced by recent dissatisfaction (availability heuristic) or the prestige of the new field (anchoring bias).
4. Seek Diverse Perspectives (Likely Skipped): Talk to professionals in the new field, career coaches, and peers who have changed careers.
5. Use Decision-Making Frameworks (Likely Skipped):Pros and Cons List: List pros and cons of staying in your current job vs. moving to the new field.SWOT Analysis: Evaluate strengths, weaknesses, opportunities, and threats for both options.
6. Evaluate Alternatives Objectively: Rank each option based on criteria like job satisfaction, financial stability, and personal growth.
7. Consult AI Tools for Analysis (Likely Skipped): Use AI tools to analyze job market data and predict future trends in the new field.
8. Make the Decision: Based on your analysis, decide whether to change careers or stay in your current job.
9. Implement the Decision: If changing careers, create a transition plan including upskilling and networking.
10. Reflect and Learn (Likely Skipped): After a year in the new career, reflect on the decision and the process. Adjust future decision-making processes based on what you learn.

To improve decision-making, individuals can focus on incorporating these often-skipped steps, even in simplified forms, to ensure a more thorough and unbiased approach.

Tools for Decision-Making

1. Define the Decision: Notion
2. Gather Information: Scite AI for academic research, Perplexity AI for web search summaries, Google Scholar Alerts
3. Identify Possible Biases: Bias Checklist, Headspace for mindfulness
4. Seek Diverse Perspectives: LinkedIn Polls, Reddit forums, AI chatbots
5. Use Decision-Making Frameworks: SWOT Analysis (focus on top 2-3 items), Pros and Cons List, Decision Matrix
6. Evaluate Alternatives Objectively: IBM Watson, Ranker
7. Consult AI Tools for Analysis: Crystal Knows, Tableau
8. Make the Decision: Trello
9. Implement the Decision: Action Plan Templates, Todoist
10. Reflect and Learn: Day One for journaling, SurveyMonkey for feedback surveys

By integrating these specific tools and techniques, the decision-making process becomes more efficient and less time-consuming while still addressing potential biases and ensuring a thorough evaluation.