Why Are Smart People So Stupid About Decisions?

Unraveling the Mysteries of Misjudgment and Ensuring Smarter Choices in Agentic AI

Nobel prize winner Daniel Kahneman passed away on March 27, 2024. He was a renowned psychologist and economist best known for his work on the psychology of judgment and decision-making, as well as behavioral economics.

His research has significantly impacted our understanding of how humans think and make decisions, often challenging the assumption of human rationality prevalent in economic theory.

This behavior is important to understand as we build agent-based systems to automate human tasks that will require human-like decisions. At XMPro, we see a future where Multi-Agent Generative Systems (MAGS) can automate tasks not just in business applications and systems but on the factory floor.

Kahneman's work, much of it conducted with his long-time collaborator Amos Tversky, has explored the systematic ways in which people make errors in judgment due to biases and heuristics.

Here's an overview of some key concepts from his work:

1. Prospect Theory

One of Kahneman's most significant contributions is Prospect Theory, developed with Amos Tversky, which describes how people choose between probabilistic alternatives that involve risk, where the probabilities of outcomes are known. This theory is a foundational element of behavioral economics and won Kahneman the Nobel Prize in Economic Sciences in 2002.

Prospect Theory highlights that people value gains and losses differently, leading to decisions that deviate from expected utility theory. For example, people are generally loss-averse, meaning they are more sensitive to losses than to gains of the same size.

“Ask people if they want to take a risk with an 80% chance of success, and most say yes. Ask instead if they’d incur the same risk with a 20% chance of failure, and many say no.”

It may seem irrational, but we put more value on not losing $100 than we do on gaining $100.

2. Heuristics and Biases

Kahneman and Tversky's research identified several heuristics, or mental shortcuts, that people use in judgment and decision-making. These heuristics include:

• Representativeness Heuristic: Judging the likelihood of an event by how closely it resembles a typical case, often leading to neglect of base-rate information.
• Availability Heuristic: Estimating the likelihood of events based on their availability in memory; if something can be recalled easily, it is seen as more frequent or probable.
• Anchoring: Relying too heavily on the first piece of information offered (the "anchor") when making decisions.

These heuristics can lead to systematic errors or biases in judgment.

3. System 1 and System 2 Thinking

In his book "Thinking, Fast and Slow", Kahneman describes two different ways the brain forms thoughts:

• System 1: Fast, automatic, intuitive, and emotional; it operates automatically and quickly, with little or no effort and no sense of voluntary control.
• System 2: Slower, more deliberative, more logical; it allocates attention to the effortful mental activities that demand it, including complex computations.

Kahneman's work shows that while System 1 can help us make decisions quickly and efficiently, it is also prone to errors and biases. System 2 is more reliable but requires more energy and is often lazy, relying on System 1 unless it's absolutely necessary to engage.

4. Overconfidence, Loss Aversion, and Endowment Effect

Kahneman's research has also explored how overconfidence in personal judgment, loss aversion (the idea that losses loom larger than gains), and the endowment effect (valuing things more highly simply because we own them) can influence decision-making in ways that deviate from rational choice theory.

These are all fascinating, highly recommended reads and explain why smart people still make stupid decisions. 

How can we prevent generative agent-based (Agentic AI) systems from making these same mistakes?

Applying Daniel Kahneman's work on judgment and decision-making to multi-agent generative systems, especially those using Large Language Models (LLMs), presents an innovative approach to designing systems that perceive, reflect, plan, and act with human-like reasoning, such as what we are building at XMPro.

Here are several ways Kahneman's insights could be leveraged to enhance the collaborative efforts and decision-making processes of such systems:

1. Incorporating System 1 and System 2 Thinking

• Adaptive Decision-making: Implement algorithms that mimic System 1 and System 2 thinking, allowing agents to switch between fast, heuristic-based decisions and slower, more deliberative processes depending on the context. This dual-process approach can make the system more flexible and efficient, adapting to both familiar and novel situations with appropriate reasoning styles.
• Error Correction Mechanisms: Use insights into the biases and errors System 1 is prone to, to develop error correction mechanisms or "debiasing" strategies within the agents. For example, before making a critical decision, an agent could automatically invoke a more deliberative, System 2-like process to check for potential biases.

2. Utilizing Heuristics and Understanding Biases

• Bias Awareness: Embedding an understanding of human cognitive biases in LLMs can help these systems better predict, interpret, and interact with human behavior. For instance, by recognizing anchoring effects, systems can adjust their negotiation strategies in real-time.
• Heuristic-Based Problem Solving: Explicitly program agents with heuristics that are beneficial for certain types of problems, enabling them to make quick decisions that are good enough for the task at hand, thus saving computational resources and time.

3. Prospect Theory for Risk Assessment

• Asymmetric Valuation: Implementing prospect theory in decision-making processes can help agents better evaluate risks and rewards from a human-like perspective, taking into account the asymmetry between the perception of gains and losses. This could be particularly useful in scenarios involving risk assessment, financial decisions, or negotiations.
• Loss Aversion and Risk Management: Agents can be designed to weigh potential losses more heavily than gains, in line with human behavior. This can make the actions and recommendations of these systems more closely aligned with human expectations and acceptance, especially in sensitive applications.

4. Multi-agent Collaboration and Negotiation

• Negotiation Strategies: By understanding and simulating human-like biases and decision-making processes, agents can become more effective negotiators, predicting and reacting to human counterparts' moves in a more sophisticated manner.
• Collaborative Goal Achievement: Systems can use an understanding of human judgment and decision-making to better coordinate among themselves and with humans, ensuring goals are achieved in a way that seems natural and intuitive to human collaborators.

5. Learning and Adaptation

• Continuous Learning: Implement feedback loops that allow agents to learn from their decision-making outcomes, adapting and updating their models to reduce biases and improve decision quality over time.
• Reflective Reasoning: Encourage models to engage in reflective reasoning about their own decision processes, akin to human metacognition, to identify when they might be relying too heavily on heuristics or biased reasoning.

By integrating these concepts from Kahneman's work, multi-agent generative systems could achieve a level of decision-making and collaboration that not only mimics human-like reasoning more closely but also effectively works alongside humans to achieve complex goals.

This approach necessitates an understanding of both the computational mechanisms underpinning LLMs and the psychological principles guiding human decision-making, bridging the gap between artificial intelligence and human cognition.

This is why we believe that simple agent-based frameworks that don't have decision Intelligence (the way we design or engineer decisions) frameworks and "Rules of Engagement" won't be suitable for the Intelligent Automation we need for industrial applications. Our focus at XMPro is to build a robust multi-agent intelligent automation system.

As we build this, we examine what technology approaches can support Daniel Kahneman's insights. One such technical approach that we incorporate is RAG.

Will Retrieval Augmented Generation (RAG) enable Heuristic-Based Problem Solving?

Retrieval-augmented generation (RAG) has the potential to facilitate heuristic-based problem-solving in AI systems, especially in contexts that benefit from integrating retrieval with generative capabilities. RAG models leverage a combination of document retrieval and text generation, typically using a transformer-based architecture for the generative part and a large corpus of documents in an organization from which relevant information can be retrieved.

This approach can enhance the model's ability to generate responses or solutions based on a wide range of sources, making it particularly suitable for applications requiring nuanced understanding and adaptation. Here's how RAG could enable heuristic-based problem-solving:

Enhancing Decision-Making with External Knowledge

RAG models can access a large amount of information, much more than what is contained in their parameters alone. By retrieving relevant documents or data in response to a query or problem, these models can apply or adapt known heuristics from similar situations documented in the data, enhancing their decision-making capabilities with external knowledge. It enables agents to make better informed decisions quicker.

Adapting Heuristics to New Contexts

Through the retrieval component, agents using RAG models can identify and adapt heuristics that are effective in similar but not identical situations. Agents can apply this capacity to adjust and apply heuristics in new contexts, which mirrors a critical aspect of human problem-solving, where individuals often need to draw on past experiences and adapt their approaches to new challenges.

Supporting Complex Problem Solving

For complex problem-solving tasks that require synthesizing information from multiple sources, RAG can retrieve and generate insights based on a broad range of data, including potentially uncovering relevant heuristics that have been applied in disparate but related contexts. This capability in agents supports a more nuanced and informed approach to applying heuristics in problem-solving.

Improving Efficiency and Creativity

By retrieving relevant information and adapting existing heuristics, RAG models can solve problems more efficiently than models that rely solely on generation without external retrieval. Moreover, this approach can lead to creative problem-solving strategies, as the combination of retrieved information and generative capabilities can produce novel solutions that a purely generative model might not.

Continuous Learning and Adaptation

Generative multi-agent with RAG models can continuously improve their problem-solving strategies by learning from the outcomes of their heuristic-based decisions. Over time, the model can refine its retrieval queries and the way it integrates retrieved information with its generative capabilities, leading to more effective heuristic-based problem solving.

Limitations and Considerations

While RAG offers promising avenues for heuristic-based problem solving, its effectiveness depends on the quality and relevance of the information it can retrieve, as well as the model's ability to integrate this information meaningfully into its generative process. Challenges include ensuring that retrieved information is accurate and contextually appropriate, and managing the potential for reinforcing biases present in the source data. These challenges also apply to human employees in tasks that require context and information in documentation, manuals, SOPs, business systems, and other external sources. The benefit of an agent-based approach is that it retains and recalls learning more consistently than humans, and the same knowledge is scalable across multiple agents.

In the fast-changing world of artificial intelligence (AI), the concept of "Agentic AI," particularly within the context of multi-agent generative systems, is gaining traction as a fascinating opportunity that merges the autonomy of AI agents with the collaborative and generative capabilities similar to human teamwork.

What does this mean for Agentic AI in Multi-Agent Generative Systems?

Agentic AI refers to systems designed with a degree of autonomy, enabling individual AI agents to make decisions, learn from interactions, and pursue goals in a dynamic environment. At their core, agents are instructions, knowledge, and actions. They can further memorize, plan, and reflect on their actions. Multi-agent generative systems (MAGS) are made up of agents that can collaborate, reason, and instruct each other. These autonomous reasoning and decision-making abilities differentiate these MAGS from simple automation tasks that may use similar tools to accomplish tasks but follow strict automation rules.

In a multi-agent generative system, each AI agent brings its own specialized capabilities or perspectives to the table, working together towards a common goal. These agents can range from language models that generate textual content, to image-generating AI that can visualize concepts, to decision-making models that strategize and plan. The "agentic" part of Agentic AI signifies that these systems are not just passive tools waiting for human input; instead, they are proactive, goal-seeking, engaging in tasks, initiating actions, and making decisions that contribute towards the collective output of the system.

The power of Agentic AI lies in its ability to harness the strengths of individual agents, enabling a level of problem-solving and creativity that is greater than the sum of its parts. For instance, in tackling complex challenges, one agent might identify a problem, another could generate creative solutions, while a third evaluates these solutions against a set of criteria. This collaborative process is dynamically coordinated, with agents continuously interacting, sharing insights, and adjusting their strategies based on real-time feedback.

Moreover, Agentic AI systems embody principles from Daniel Kahneman’s work on human judgment and decision-making, such as balancing fast, intuitive decisions with slower, more deliberate reasoning. By incorporating these aspects, multi-agent systems can navigate tasks with a nuanced understanding of both efficiency and depth, much like a well-rounded human team would.

The impact of Behavioral Reasoning for Intelligent Automation is profound

The implications of Agentic AI for industrial intelligent automation and business process management are profound. These systems could accelerate innovation, enhance problem-solving, and open new avenues for exploration that were previously unimaginable. Agentic AI not only allows us to improve productivity, it enables us to rethink how we get work done.

Let's get agents to do for industrial automation what telephone exchanges did for communications. We replaced repetitive human switchboard operator tasks with a completely new way of automating communication at scale. We didn't merely automate a human task; we changed the process and decision intelligence that support it. Not only did we improve telephone communications, but we have more people working in telecoms than ever. MAGS have to potential to do that for many industries and at XMPro we are building MAGS that support industrial applications and use cases.

Thank you, Daniel Kahneman, for your contribution to understanding what we have to consider when building the next generation of MAGS.

Let's make the future a collaboration between Smart People enabling Smart Agents to make Smart Decisions.