How do you engage effectively across deep disagreement without shutting down the conversation?This week on If/Then, we’re sharing an episode from our colleagues at Stanford Legal, the podcast from Stanford Law School that looks at the cases, questions, and conflicts shaping public life.In a world where confidence is rewarded and humility can feel like a liability, Stanford Law professor Robert MacCoun argues for something radical: fewer unwavering opinions, more critical reflection, and a better way to disagree. On Stanford Legal, MacCoun joins co-hosts Pam Karlan and Diego Zambrano for a conversation about how “habits of mind” borrowed from science can help citizens, lawyers, and policymakers think more clearly, listen more carefully, and build better public debate around difficult questions that don’t have easy answers.Trained as a social psychologist, MacCoun's work sits at the intersection of law, science, and public policy, with decades of research on decision-making, bias, and the social dynamics that shape how evidence is interpreted. In the episode, he draws on his most recent book, Third Millennium Thinking: Creating Sense in a World of Nonsense, co-authored with Nobel Prize–winning physicist Saul Perlmutter and philosopher John Campbell, to explain why probabilistic thinking, intellectual humility, and what he calls an “opinion diet” are essential tools for modern civic life.Related Content:Robert MacCoun faculty profileThird Millenium ThinkingStanford Legal PodcastChapters:00:00:00 Introduction00:01:23 The course, the book, & what motivated it00:04:06 Habits of mind for better decision-making00:06:20 Probabilistic thinking and intellectual humility00:09:57 An “opinion diet”00:12:16 Reasonable doubt, community, & collective judgment00:14:13 Scientific optimism and the problem of cynicism00:17:31 Why trust in science has eroded00:20:10 Law, science, & the value of procedure00:22:50 Steel-manning the other side00:24:58 Public policy as provisional problem-solving00:30:07 Deliberative democracy and informed public debate00:32:03 Conclusion See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

“Humans manage to do so much with surprisingly little,” says Douglas Guilbeault, an assistant professor of organizational behavior at Stanford Graduate School of Business. “Whereas AI, by comparison, is doing relatively little, but with so much power, so much compute, so many resources, and by comparison, relatively fewer constraints.”On a bonus episode of the If/Then podcast, Guilbeault describes the implications of his recent work. Although he readily acknowledges that AI is “increasingly able to do quite a lot,” Guilbeault and his colleagues believe they have identified a key principle that distinguishes human intelligence from machine intelligence — and one which illuminates the limitations of machine thinking. Although some researchers and AI boosters believe both humans and AI learn via optimization, Guilbeault and his colleagues have shown that another process more accurately captures how people distill the seemingly infinite complexity of the world and act based on limited information. “You encounter a lot of noise, a lot of chaos, a lot of randomness,” Guilbeault says. “We somehow figure out how to make meaning and establish strong understandings from within that.”What limitations have you encountered in your work with AI? Share your story with us at [email protected] Content:Douglas Guilbeault faculty profileRead "A Simple Threshold Captures the Social Learning of Conventions" hereChapters:00:00:00 Introduction00:01:40 Why human learning matters for AI00:05:03 Satisficing and the limits of optimization00:06:41 Why LLMs learn differently from humans00:09:58 The stakes of AI hype00:13:11 “Humanity has had a good run”00:15:19 Intuition, insight, & conceptual leaps00:17:38 Beyond statistics: metaphor, vibes, & reasoning00:19:39 A simple rule for social learning00:21:18 Is there a ceiling for AI?00:23:00 Randomness, disorder, & the path to insight00:25:00 What an optimization mindset leaves out00:27:54 Conclusion If/Then, from Stanford GSB, features conversations with faculty that explore how their research deepens our understanding of business and leadership. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Abundance, anyone? While the risks posed by artificial intelligence are extraordinary, so is the potential, says professor of economics Chad Jones.

“Great leaders are people who think of themselves as trustees of other people’s time,” says Huggy Rao.

Seeing the world differently can be costly, but it can also provide a competitive edge.

The moment we see someone as an individual rather than a category, we become more likely to find common ground.

“We spend a lot of time talking about gender inequality through the lens of women’s disadvantage,” she says. “I think that many of the problems that we’re seeing today… are actually bound up in masculinity.”

Steven Callander has spent years building a mathematical framework to answer the question of how people learn from experience. “Here in Silicon Valley, the expression that you learn from failure is very widespread and very intuitive. But the question is… what do you learn? How do you optimally learn from that experience?”In this episode, Callander, the Herbert Hoover Professor of Public and Private Management and Professor of Political Economy at Stanford Graduate School of Business, explains the hidden, deceptively simple logic of correlated learning — and it may change how you think about finding the right job, the right market, or the right strategy. “It fascinates me and I can't stop thinking about it,” he says.  Has theory made an impact on your life? Tell us more at [email protected] Content:Steven Callander faculty profileHow to Turn Old Ideas Into Creative Solutions to Modern ProblemsWhat We’re Still Learning from Silicon Valley’s Bank CollapseChapters:00:00 Ann Miura-Ko on learning and the search for patterns in Venture capital02:51 Introduction05:23 What is correlated learning?06:40 Where does this research apply in the real world?09:28 Brownian Motion12:45 Steven Callander’s Framework15:25 Examples of correlated learning when seeking expert advice20:53 Applying correlated learning23:57 Why correlated learning research?24:51 ConclusionIf/Then, from Stanford GSB, features conversations with faculty that explore how their research deepens our understanding of business and leadership. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Seemingly unrelated activities — like taking a soccer penalty kick or crafting an online dating profile — involve an embedded economics.

If/Then Season 3 is coming next week. GSB faculty explore innovative research that deepens our understanding of business and leadership.