AI, Quantum Cognition, and Decision Making

Future Point Digital is research-based consultancy positioned at the intersection of artificial intelligence and humanity. We employ a human-centric, interdisciplinary, and outcomes-based approach to augmenting human and machine capabilities to create super intelligence. Follow us at: www.futurepointdigital.com.

Life is often about the quality of the decisions we make, such as what type of friendships to establish, what career choices to make, whether to take a company public or stay private, whether to enter new markets, etc. etc. Sometimes the decisions are straightforward, posing little risk, while others are extremely complex and fraught with serious consequences if suboptimal choices are selected.

The interdisciplinary field of decision science, sometimes recognized as a sub-domain of management science, among other fields, has formally attempted to quantify the decision-making process since at least the mid-twentieth century. Drawing on insights from psychology, sociology, anthropology, economics, mathematics, philosophy, business, and statistics, researchers have developed numerous decision-making frameworks and models. These include the Rational Decision Making Model, the Bounded Rationality Model, Prospect Theory, Behavioral Decision Theory, and Expected Utility Theory, among many others (Kahneman & Tversky, 2000; Bell, Raiffa, & Tversky, 1988).

While these models are effective and useful in certain contexts, they have clear limitations and can sometimes fall significantly short due to various reasons. A primary reason is that many, if not all, of these models assume a level of simplicity and clarity that often does not reflect the complexity of real-world situations.

Yet, as humans, this is how we often respond when faced with phenomenon that exceed our understanding. This inclination aligns with what psychologist Alfred Adler might have described as fictional finalism—whereby humans are propelled more by the desire to foresee future events, even if based on incomplete information and risky speculation, than by the constraints of past experiences and solid knowledge (Ansbacher & Ansbacher, 1956). Faced with phenomena we cannot fully grasp, we instinctively fill in the gaps, making assumptions in order to navigate around rather than through anything that might produce troubling cognitive dissonance. Perhaps this tendency is Darwian in nature —by constantly guiding our reach to exceed our grasp, humans have achieved a lot —including survival.

Nevertheless, the more assumptions contained in any model, the higher the risk of fallibility; and most decision models turn on the assumption that decision makers have the ability to process all relevant information and calculate the best possible outcome. Yet we know, based on veritable mountains of empirical findings, that cognitive biases, emotions, and bounded rationality often limit human ability to make optimal decisions (Kahneman, 2011; Simon, 1955). We also know that making decisions that are not supported by established models can sometimes result in outcomes deemed to be more optimal than those prescribed by such models. (One famous example is Steve Jobs' decision to proceed with the development of the iPod and iPhone, despite contrary indications in analytical models and expert advice; Isaacson, 2011).

So what are these models missing? What is it exactly that we are unable capture or quantify about human nature with respect to rational decision making? Why is it that the more precisely we define and measure a particular aspect of the human decision making process, the more elusive other aspects become? And perhaps most importantly, in what other fields of study have we witnessed similar instances of ambiguous and unpredictable behavior that might serve to better inform our understanding of human cognitive processes —specifically, with respect to decision making?

Enter Quantum Cognition 

Quantum cognition is a field of study that applies the mathematical frameworks and principles of quantum mechanics—a branch of physics that deals with the behavior of particles at the smallest scales—to understand and model cognitive processes and decision-making in humans. This interdisciplinary approach seeks to explain phenomena in cognition that are difficult to account for with classical probability theory and cognitive models.

The concept is based on the premise that the probabilistic nature of quantum mechanics offers a better fit for modeling certain aspects of human thought and decision-making. Unlike classical probability theory, which assumes that probabilities are additive and decisions are made based on a fixed set of preferences, quantum probability allows for the representation of changing states and preferences, reflecting the fluid and sometimes inconsistent nature of human thought.

One key concept in the quantum cognition model is superposition, which is analogous to quantum superposition, where a particle can exist in multiple states simultaneously until measured. Similarly, quantum cognition suggests that people can hold conflicting beliefs or consider multiple options at once until a decision "collapses" into a single outcome.

In essence, quantum cognition researchers have found that people's decisions and beliefs often do not conform to the expected laws of classical probability. For example, in situations involving uncertainty and ambiguity, people's reasoning patterns exhibit characteristics like superposition (as referenced above -entertaining multiple, potentially conflicting options simultaneously) and contextuality (where the outcome of a decision is influenced by the way questions are framed or the context in which choices are presented), which are more accurately described by quantum theory than by classical theories (Pothos & Busemeyer, 2013; Yukalov & Sornette, 2010).

Practical Applications

OK great, but what does all of this mean (potentially) in practical terms? How can this improve our lives as business leaders, members of society, or personally? In other words, so what?!

Well, one way to answer that question is to consider how advancements in quantum cognition could have a variety of implications across different fields, offering new perspectives and tools for understanding complex human behaviors. Consider the following:

• Psychology: Quantum cognition can provide a framework for understanding decisions that don't conform to classical logical norms, such as when people change their preferences based on context or display uncertainty in their choices. It could revolutionize therapeutic techniques by helping psychologists better understand how people reconcile conflicting thoughts and emotions.

• Sociology: In sociology, quantum cognition models can offer insights into social phenomena like opinion formation, cultural shifts, and the dynamics of social networks by showing how individual cognitive processes interact with group dynamics.

• Management Science: For management science, quantum cognition could inform models of consumer behavior, decision-making under uncertainty, and negotiation strategies. Understanding how people make choices can lead to better marketing, management of human resources, and organizational behavior strategies.

• Leadership: Leaders could benefit from quantum cognition by gaining a better grasp of how people process complex and contradictory information, aiding in conflict resolution, strategy development, and fostering innovative thinking within teams.

So, in practical terms (albeit from a high-level perspective) embracing a quantum cognition approach means acknowledging that human thought and behavior are not always straightforward or predictable. Instead, they’re often influenced by a multitude of factors and can change dynamically, frequently defying classical logic. By using quantum models, professionals in the above (and other) fields can develop more flexible and accurate predictions, as well as design interventions that might align more accurately and robustly with the true nature of human cognition (Busemeyer & Bruza, 2012; Khrennikov, 2010; Haven & Khrennikov, 2013; Pothos & Busemeyer, 2013; Wang, et al, 2014).

What Role Might Generative AI Play in Advancing Quantum Cognition?

According to prompted analysis generated by OpenAI's ChatGPT (2024), generative AI, with its capacity to simulate complex systems and generate data-driven insights, has the potential to significantly advance the field of quantum cognition in several key ways:

• Modeling and Simulation: Generative AI can be used to create sophisticated models of quantum cognitive processes. By simulating how humans make decisions based on principles of quantum mechanics—such as superposition, entanglement, and interference—researchers can explore the nuances of cognitive phenomena that classical models fail to capture. This could lead to a deeper understanding of human thought processes and decision-making.

• Data Generation and Analysis: Generative AI can produce vast amounts of synthetic data that mimic complex cognitive behaviors. This capability is particularly useful for testing quantum cognition theories under various conditions and scenarios, which might be difficult or impossible to replicate in real-world experiments. Moreover, AI-driven analysis of this data can uncover patterns and correlations that inform the development of quantum cognitive models.

• Enhancing Experimental Design: AI can assist researchers in designing experiments that more accurately test the predictions of quantum cognition theories. By analyzing existing data, AI can suggest variables and parameters that are most likely to yield informative results, thus refining experimental methodologies and improving the quality of empirical evidence.

• Interdisciplinary Integration: Generative AI facilitates the integration of insights from quantum physics, cognitive psychology, neuroscience, and computer science. This interdisciplinary approach is crucial for the advancement of quantum cognition, as it allows for the exploration of cognitive phenomena through multiple lenses and methodologies.

• Personalization of Cognitive Models: Generative AI's ability to learn and adapt from data makes it possible to develop personalized models of cognitive behavior. This could lead to quantum cognition-based applications that are tailored to individual decision-making styles, enhancing the effectiveness of educational tools, therapeutic interventions, and even user interfaces.

• Improving Computational Tools: The computational demands of quantum cognition models, especially those involving complex calculations of probabilities and simulations of entangled systems, can be substantial. Generative AI can contribute to the development of more efficient computational tools and algorithms, making it easier for researchers to explore and validate quantum cognitive theories.

• Bridging Theory and Practice: By generating actionable insights from quantum cognition theories, generative AI can help bridge the gap between abstract theoretical models and practical applications. This might include developing better decision-making tools, enhancing cognitive training programs, or improving strategies for managing uncertainty and ambiguity in various professional fields.

As generative AI continues to evolve, its role in expanding the frontiers of quantum cognition is likely to grow, offering exciting possibilities for uncovering the quantum underpinnings of human thought and behavior. This could lead to a more complete understanding of how humans make decisions, in a variety of situations, leading to higher quality outcomes.

How might FuturePoint Digital help your organization explore exciting, emerging concepts in science and technology? Follow us at www.futurepointdigital.com, or contact us via email at [email protected].

About the Author: David Ragland is a former senior technology executive and an adjunct professor of management. He serves as a partner at FuturePoint Digital, a research-based technology consultancy specializing in strategy, advisory, and educational services for global clients. David earned his Doctorate in Business Administration from IE University in Madrid, Spain, and a Master of Science in Information and Telecommunications Systems from Johns Hopkins University, where he was honored with the Edward J. Stegman Award for Academic Excellence. He holds an undergraduate degree in Psychology from James Madison University and has completed a certificate in Artificial Intelligence and Business Strategy at MIT. His research focuses on the intersection of emerging technology with organizational and societal dynamics.

References

Ansbacher, H. L., & Ansbacher, R. R. (Eds.). (1956). The individual psychology of Alfred Adler: A systematic presentation in selections from his writings. Harper & Row.

Bell, D. E., Raiffa, H., & Tversky, A. (Eds.). (1988). Decision making: Descriptive, normative, and prescriptive interactions. Cambridge University

Busemeyer, J. R., & Bruza, P. D. (2012). Quantum models of cognition and decision. Cambridge University Press.

Fitzgerald, F. S. (1936). The Crack-Up. Esquire.

Haven, E., & Khrennikov, A. (2013). Quantum social science. Cambridge University Press.

Isaacson, W. (2011). Steve Jobs. Simon & Schuster.

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

Kahneman, D., & Tversky, A. (Eds.). (2000). Choices, values, and frames. Cambridge University Press.

Khrennikov, A. (2010). Ubiquitous quantum structure: From psychology to finance. Springer.

OpenAI. (2024). Insights on the advancement of quantum cognition through generative AI [Generated content]. ChatGPT.

Pothos, E. M., & Busemeyer, J. R. (2013). Can quantum probability provide a new direction for cognitive modeling? Behavioral and Brain Sciences, 36(3), 255-274.

Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99-118.

Senge, P. M. (1990). The Fifth Discipline: The Art & Practice of The Learning Organization. Currency Doubleday.

Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty: Heuristics and biases. Cambridge University Press.

Tichy, N. M., & Sherman, S. (1993). Control Your Destiny or Someone Else Will: How Jack Welch is Making General Electric the World's Most Competitive Company. Doubleday

Wang, Z., Solloway, T., Shiffrin, R. M., & Busemeyer, J. R. (2014). Context effects produced by question orders reveal quantum nature of human judgments. Proceedings of the National Academy of Sciences, 111(26), 9431-9436.

Yukalov, V. I., & Sornette, D. (2010). Mathematical structure of quantum decision theory. Advances in Complex Systems, 13(05), 659-698.

Thanks for reading FuturePoint Digital White Papers! Subscribe for free to receive new posts and support my work.