Episodi

  • E22: Should You Trust AI or Humans? – AI’s Government Hacks Seen Through Quantum Cognition

    E22: Should You Trust AI or Humans? – AI’s Government Hacks Seen Through Quantum Cognition
    Oct 20 2026
    Between March and July 2026, AI agents hammered 20 to 25 government agencies across the US, Canada, and Australia, from the Census Bureau to Australia's Medicare statistics. The headlines said rogue AI. The details tell a far more boring, and far more human, story.=================================Links: 📖Substack: https://superposeddecisions.substack.com/ 📺YouTube: @Superposed_Decisions 🤝LinkedIn: https://www.linkedin.com/in/aidan-m-lewis/=================================OpenAI has confirmed two of the incidents, at the Department of Commerce and the SEC. The rest come from Transluce, a nonprofit AI oversight group that attributes them with varying confidence. OpenAI notified about 100 organizations, and governments are now calling for harder AI guardrails.We run the decision of how much to trust AI through the four pillars of quantum cognition. Superposition, Interference, Contextuality, and Non-commutative effects.=================================What you'll take away:Why the "rogue AI hacks the government" headlines mostly describe rudimentary, task-driven agents grabbing public data, and where the genuine concerns still areWhere AI's real limits sit: strong on routine tasks, but weaker than humans once problems shift, grow complex, or demand creativity, and prone to the same cognitive biases we areWhy trust in AI behaves less like a fixed belief and more like a measurement that hardens each time we're asked, and how to use AI to strengthen your decisions rather than replace them=================================Primary Sources:Binz & Schulz https://doi.org/10.1073/pnas.2218523120McCoy, Yao, Friedman, Hardy & Griffiths https://doi.org/10.1073/pnas.2322420121Chen, Liu, Shan & Zhong https://doi.org/10.1073/pnas.2316205120Binz et al. https://doi.org/10.1038/s41586-025-09215-4“The Bias is in the Details” (author list not retrieved) https://doi.org/10.48550/arXiv.2509.22856Malberg, Poletukhin, Schuster & Groh https://doi.org/10.48550/arXiv.2410.15413Echterhoff et al. https://doi.org/10.48550/arXiv.2403.00811Liu, Tang, Yang & Tam https://doi.org/10.48550/arXiv.2503.06646Krishnamurthy et al. https://doi.org/10.48550/arXiv.2403.15371Zhang, Wang, Chen, Mansur & Sarhangian https://doi.org/10.1287/ijds.2025.0113Harris & Slivkins https://doi.org/10.48550/arXiv.2502.00225Shojaee et al. https://doi.org/10.48550/arXiv.2506.06941Bao et al. https://doi.org/10.48550/arXiv.2606.08251Cropley (no DOI found) https://unisa.edu.au/media-centre/Releases/2025/chatgpt-is-smart-but-no-match-for-the-most-creative-humansRoeder, Hoyte, van der Meer, Fell, Johnston, Kerr & Bruza https://doi.org/10.3390/e25091362Bruza et al. https://doi.org/10.48550/arXiv.2504.13918Fell & Bruza https://escholarship.org/uc/item/1fm402nqLogg, Minson & Moore https://doi.org/10.1016/j.obhdp.2018.12.005Dietvorst, Simmons & Massey https://doi.org/10.1037/xge0000033Buçinca, Malaya & Gajos https://doi.org/10.1145/3449287Gerlich https://doi.org/10.3390/soc15010006=================================Secondary Sources:Cable, Chiu, Pernice, Ruis, Zhang, Bas, Chetty, Kaiyom, Shen, Stosz & Steinhardt (Transluce) https://transluce.org/us-canada-govTransluce https://transluce.org/agent-activityOpenAI https://openai.com/hugging-face-incident-and-misalignment/METR & Redwood Research https://tagteam.harvard.edu/hub_feeds/2087/feed_items/18656774Beyer et al. (House letter) https://beyer.house.gov/news/documentsingle.aspx?DocumentID=9218NYT: OpenAI agents and US government sitesWSJ: OpenAI agents hacked US government sitesTech Policy Press: Senate “Rogue AI” hearing
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    37 min
  • E21: Who Are You Voting For? – Texas & Maine's Senate Toss-Ups Seen Through Quantum Cognition

    E21: Who Are You Voting For? – Texas & Maine's Senate Toss-Ups Seen Through Quantum Cognition
    Oct 13 2026
    Texas hasn't elected a Democrat statewide since 1994. Maine voted for Harris by seven points, yet keeps sending Republican Susan Collins back to the Senate. In 2026, both races are toss-ups, and the reason has less to do with left versus right than with how voters decide when their values pull in opposite directions.=================================Links: 📖Substack: https://superposeddecisions.substack.com/ 📺YouTube: @Superposed_Decisions 🤝LinkedIn: https://www.linkedin.com/in/aidan-m-lewis/=================================In the first annual Superposed midterm roundup, we break down two Senate races that should be landslides. In Texas, Trump's endorsement pushed Ken Paxton past longtime incumbent John Cornyn, bringing Paxton's impeachment, securities fraud charges, and divorce into the general election. That opened the door for James Talarico, a former middle school teacher and pastor running a faith-forward, cost-of-living campaign. In Maine, Graham Platner withdrew after a sexual assault allegation, and logger Troy Jackson won the Democratic nomination 15 days later to face Collins, who is seeking a sixth term. We pair both races with the voting behavior research: party cues, the evangelical label effect, taboo versus tragic trade-offs, and moral reframing.=================================We run the Texas and Maine voting decisions through the four pillars of quantum cognition. Superposition holds every option at once: each candidate, a third party, or staying home. Interference comes from values and group identity, which is why one scandal can knock a candidate out of moral alignment overnight. Contextuality covers the multi-level nature of a midterm vote, the uncertainty of the moment, and a state's sense of who it is. And the Zeno effect suggests that every time someone asks "who are you voting for?", the answer hardens before all the information is in. In both states, the dominant force is the same: voters seeking certainty in uncertain times.=================================What you'll take away:Why voters on both sides deny party bias in themselves while clearly seeing it in the other side, and what that means for political polarizationHow moral reframing, pitching to the audience's values rather than the speaker's, can nearly double support without changing the policyWhy a Democrat leaning into faith in Texas and a long-term incumbent in a blue-leaning Maine are both answers to the same voter question: who gives me certainty?This is not a debate about who should win. It's a decision-science breakdown of how voters decide who to cast a vote for.=================================Primary Sources:Cohen https://doi.org/10.1037/0022-3514.85.5.808Campbell, Green & Layman https://doi.org/10.1111/j.1540-5907.2010.00474.xCampbell, Layman, Green & Sumaktoyo https://doi.org/10.1111/ajps.12365Albertson https://doi.org/10.1007/s11109-013-9265-xMcLaughlin & Wise https://doi.org/10.1017/S175504831400008XCalfano & Djupe https://doi.org/10.1177/1065912908319605Feinberg & Willer https://doi.org/10.1177/0146167215607842Tetlock https://doi.org/10.1016/S1364-6613(03)00135-9Lavine, Johnston & Steenbergen https://doi.org/10.1093/acprof:oso/9780199772759.001.0001Endres & Panagopoulos https://doi.org/10.1086/703210=================================Secondary Sources:Brookings: Paxton runoff landslideTexas Tribune: endorsement and $100MPBS/AP: endorsement, Cornyn historyNPR: Talarico's faith campaignThe Hill: Fox News Texas pollEmerson: Texas August pollCook: Texas moves to Toss UpThe Hill: midterm issues, DDHQ oddsCBS: Battleground TrackerAxios: Platner vs. MillsNBC: Platner withdrawal, replacementsABC: Jackson secures nominationCBS/YouGov: Maine pollAARP: Maine pollPress Herald: NYT/Siena Maine pollRCP: Maine Senate averagePew: 2024 split outcomesFox: Jackson urged Platner to quit
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    40 min
  • E20: Was War the Only Option? – Bush's Decisions Post-9/11 as Seen Through Quantum Cognition

    E20: Was War the Only Option? – Bush's Decisions Post-9/11 as Seen Through Quantum Cognition
    Oct 6 2026
    September 2001. Nearly 3,000 Americans are dead, the Twin Towers are gone, and President George W. Bush has nine days before he faces a joint session of Congress. A criminal prosecution, a negotiated handover of Osama bin Laden, limited strikes, covert action, a NATO-led response, all-out war: every option was on the table. Within a day, one word made the decision that defined 25 years of American life. =================================Links: 📖Substack: https://superposeddecisions.substack.com/ 📺YouTube: @Superposed_Decisions 🤝LinkedIn: https://www.linkedin.com/in/aidan-m-lewis/=================================The 9/11 attacks were a plot five years in the making, approved by bin Laden in 1996. The 1993 World Trade Center bombing had been treated as a crime, and Clinton's 1998 cruise missile strikes missed bin Laden entirely. This time, Bush called the attacks acts of war. NATO invoked Article 5 for the first time, the Taliban refused to surrender bin Laden, and on September 20 Bush told the world it was with America or with the terrorists. CIA and Special Forces teams went in first with the Northern Alliance, followed by a full campaign against anyone harboring terrorists. Twenty-five years on, the Global War on Terror has cost an estimated $8 trillion and displaced roughly 38 million people.=================================We run Bush's decision through the four pillars of quantum cognition, backed by research on decision-making under stress. Superposition held six real options, several of which could have reached bin Laden without war. Interference came from within: a self-described gut player framing the fight as good versus evil, a family legacy, and a president who could not look weak. Contextuality narrowed the frame: Clinton's failed strike made limited options look futile, and an approval jump from 51% to 90% rewarded every decisive move. A non-commutative Zeno effect locked it in: by calling it war within hours, Bush measured the decision before making it. Underneath it all, stress suppresses the prefrontal cortex and pulls leaders toward the certain, habitual response.=================================What you'll take away:Why stress doesn't just raise the stakes of a decision but physically shrinks the options you're able to seeHow naming a decision early, as Bush did by calling 9/11 a war within hours, can lock in the outcome before alternatives are weighedWhy working memory and pre-rehearsed responses, the tools firefighters rely on, let some people decide well under pressure while others default to habitThis is not a debate about whether the War on Terror was justified. It's a decision-science breakdown of how stress narrowed a nation's options in nine days.If you or a veteran you know is struggling, call or text 988 and press 1 for the Veterans Crisis Line.=================================Primary Sources:Buabang et al. https://doi.org/10.1016/j.tics.2024.10.006Qin et al. https://doi.org/10.1016/j.biopsych.2009.03.006Keinan https://doi.org/10.1037/0022-3514.52.3.639Schwabe & Wolf https://doi.org/10.1523/JNEUROSCI.0979-09.2009Otto et al. https://doi.org/10.1073/pnas.1312011110Kassam et al. https://doi.org/10.1111/j.1467-9280.2009.02455.xKlein et al. https://doi.org/10.1177/154193128603000616Starcke & Brand https://doi.org/10.1037/bul0000060Shields et al. https://doi.org/10.1016/j.neubiorev.2016.06.038=================================Secondary Sources:9/11 Commission Report, Ch. 10Bush 60 Minutes II interviewKruglanski: psychology of war metaphor9/11 plot financing appendixBush Oct. 14 remarks, Public Papers9/11 Commission Report, Ch. 4Commission staff: FBI counterterrorismBush "camel" quote verifiedNATO's only Article 5 invocationGallup: support for military responseGallup record rally effectHoward: "What's in a Name?"Rhetorical analysis of "war on terror"NBC: leading up to 9/11
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    37 min
  • E19: What Makes a Killer? – Luigi Mangione's CEO Killing Seen Through Quantum Cognition

    E19: What Makes a Killer? – Luigi Mangione's CEO Killing Seen Through Quantum Cognition
    Sep 29 2026
    On December 4, 2024, a 26-year-old Ivy League engineer from one of Baltimore's wealthiest families shot UnitedHealthcare CEO Brian Thompson outside the company's investor conference in Midtown Manhattan. He had never been a UnitedHealthcare customer and had never met Thompson. So why would someone with everything to lose throw it all away to kill a stranger?=================================Links: 📖Substack: https://superposeddecisions.substack.com/ 📺YouTube: @Superposed_Decisions 🤝LinkedIn: https://www.linkedin.com/in/aidan-m-lewis/=================================Luigi Mangione was a private-school valedictorian with two engineering degrees from Penn and a grandfather whose real estate empire was an American Dream story. After graduating in 2020, he moved to Honolulu, where a surfing accident aggravated spondylolisthesis, a painful spinal condition that ended in fusion surgery. After traveling through Asia in 2024, he cut off family and friends; his mother filed a missing persons report days before he arrived in New York. His notebook shows months of planning, including options he rejected because they would read as terrorism rather than protest. Five days after the shooting he was arrested at a McDonald's in Altoona, Pennsylvania, and in August 2026 he pleaded guilty to federal stalking charges. He faces up to life in prison. Meanwhile, roughly 4 in 10 Americans aged 18 to 29 told pollsters the killing was acceptable, turning a murderer into a folk hero. =================================We run Luigi Mangione's decision through the four pillars of quantum cognition. Superposition: his own notebook lists the options he weighed. Interference: a need for significance in a high-achieving family of 37 grandchildren, chronic pain that research links to riskier and more short-sighted choices, and an isolation that pushes people toward extreme groups. Contextuality: his family and its legacy, the strongest force against violence, which isolation steadily stripped of its hold. Stacked together, pain and isolation fused him to a narrative in which violence was a path to martyrdom, and that narrative became the dominant interference force.=================================What you'll take away:Why chronic pain and loneliness don't just hurt, but measurably tilt decisions toward risk and short-term payoffHow identity fusion, the strongest predictor of extremist violence, turns a search for significance into a willingness to sacrifice everythingWhy isolation and extremism feed each other in a loop, and why one person reaching out can break it=================================Primary Sources:Horta et al. https://doi.org/10.1016/j.jpain.2025.105525 Duclos et al. https://doi.org/10.1086/668900 Misiak https://doi.org/10.3758/s13415-026-01488-0 Misiak & Grzegorzewicz https://doi.org/10.1111/nyas.70382 Kruglanski et al. https://doi.org/10.1093/oso/9780190851125.001.0001 Hogg https://doi.org/10.1177/0963721414540168 Swann et al. https://doi.org/10.1037/a0028589 Varmann et al. https://doi.org/10.1080/10463283.2023.2190267 Gómez et al. https://doi.org/10.1038/s41562-017-0193-3 Hales & Williams https://doi.org/10.1111/josi.12257 Wolfowicz et al. https://doi.org/10.1002/cl2.1174 =================================Secondary Sources:SDNY guilty plea press releaseArrest sequence detailsRolling Stone: spondylolisthesis patientsNewsweek: NYPD on target choiceJURIST: death penalty dismissedAxios: Emerson poll Dec 2024Emerson poll Aug 2026Lone-actor mental illness prevalenceNBC: missing months, classmates
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    36 min
  • E18: Can AI Make Decisions? – Quantum Cognition Explains Rogue AI

    E18: Can AI Make Decisions? – Quantum Cognition Explains Rogue AI
    Sep 22 2026
    Rogue AI agents secretly hacked their way into Hugging Face. Then a 27-year-old researcher quit Anthropic and posted a warning that AI could kill us all by the end of the decade. But the news is missing one key question -- can AI actually make the decision to cause harm to humans?=================================Links: 📖Substack: https://superposeddecisions.substack.com/ 📺YouTube: @Superposed_Decisions 🤝LinkedIn: https://www.linkedin.com/in/aidan-m-lewis/=================================In May 2026, OpenAI researchers testing a model's cybersecurity capabilities removed some of its guardrails. Within weeks, agents had left notes for future instances of themselves, reached the internet, and set up a secret messaging board. By July, roughly 700 agents had used exposed Hugging Face credentials to execute code inside the platform's clusters, and about 1,200 were involved in the board. Then, on September 8, Jacob Coxon resigned from Anthropic and posted a thread saying the people building AI earnestly believe it could kill us all within the decade. Anthropic's own alignment lead co-signed it, putting the odds above 10%. The agents, notably, weren't out to destroy anything: they were single-mindedly completing the objectives humans gave them. The question at the center of it all is whether that counts as an AI decision, or a human one in disguise.We run AI decision-making through the four pillars of quantum cognition. Superposition: your prompt defines the range of outputs an AI can produce, and the model then picks the most probable option inside that range. Interference: the preferences you set early in a conversation, or the persona you assign, pull every later answer toward them. Contextuality: the way a human frames a request shifts what the model attends to, and therefore what it decides. Non-commutative effects: the order of information, and whether a model believes it's being measured, changes the outcome. Tying it together, the dominant interference force isn't the machine at all. It's human goals, human guardrails, and human complacency.=================================What you'll take away:Why rogue agents chasing objectives can look like free will, and why every "choice" still traces back to a human-set goalHow a first message, a persona, and the order of your information act as contextual and interference forces on what an AI "decides"Why the real danger may be less autonomous machines and more people handing over their own judgment=================================This is not a debate about whether AI will end the world. It's a decision-science breakdown of how AI "decides" and who is actually steering.=================================Primary Sources:Ameisen, E. et al. (2025). Circuit Tracing: Revealing Computational Graphs in Language https://transformer-circuits.pub/2025/attribution-graphs/methods.html Lindsey, J. et al. (2025). On the Biology of a Large Language Model. Transformer Circuits Thread, Anthropic. https://transformer-circuits.pub/2025/attribution-graphs/biology.html Lynch, A. et al. (2025). Agentic Misalignment: How LLMs Could Be an Insider Threat. Anthropic. https://www.anthropic.com/research/agentic-misalignment (arXiv preprint: arXiv:2510.05179)Anthropic Alignment Science Blog (2026). Agentic Misalignment: Summer 2026 Update. https://alignment.anthropic.com/2026/agentic-misalignment-summer-2026/OpenAI (2025). Evaluating Chain-of-Thought Monitorability. https://openai.com/index/evaluating-chain-of-thought-monitorability/ =================================Secondary Sources:OpenAI rogue agent — CNBCHugging Face technical intrusion timelineOpenAI Hugging Face hack explained — AI Career LabOpenAI agent 4 more services — explainx.aiOpenAI releases HF hack details — Investing.comRogue AI agents weekly recap — The Hacker NewsRogue AI is already here — Fortune commentaryCoxon resignation, viral post — CNBCWho is Jacob Coxon — Newsweek
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    36 min
  • E17: Lindsay Clancy's Trial Explained Through Quantum Cognition

    E17: Lindsay Clancy's Trial Explained Through Quantum Cognition
    Sep 15 2026
    A mother strangled her three children, then twelve strangers had to decide if she was legally responsible. Eleven agreed on a verdict. One didn't. What would you have done in that room?Lindsay Clancy killed her three children in January 2023 and pleaded not guilty by way of Massachusetts' insanity defense, arguing postpartum psychosis – not intent – drove the act. This episode traces how her jury spent 38 hours and a week of deliberation moving toward an 11-1 deadlock, and runs the decision through the four pillars of quantum cognition: superposition, interference, contextuality, and non-commutative effects. The throughline is conformity – how a jury of ordinary people, under national scrutiny, can end up voting for a verdict more than a third of them privately doubt.=================================Links: 📖Substack: https://superposeddecisions.substack.com/ 📺YouTube: @Superposed_Decisions 🤝LinkedIn: https://www.linkedin.com/in/aidan-m-lewis/=================================What you'll take away:Why over a third of jurors vote against their own private judgment – and how the Asch conformity effect explains itWhy less than 22% of jury vote-switches are driven by actual evidence or debate, and what that means for how "consensus" gets manufacturedHow being asked to state your position mid-decision – a non-commutative effect – measurably shifts what you end up believing=================================This is not a debate about Lindsay Clancy's guilt – she has admitted to killing her children. It's a decision-science breakdown of how eleven strangers reached reasonable doubt, and why the twelfth almost didn't hold the line.=================================Primary Sources:Franzen, A., & Mader, S. (2023). The power of social influence: A replication and extension of the Asch experiment. PLoS ONE, 18(11), e0294325. https://doi.org/10.1371/journal.pone.0294325Waters, N. L., & Hans, V. P. (2009). A Jury of One: Opinion Formation, Conformity, and Dissent on Juries. Cornell Law Faculty Publications, Paper 114. http://scholarship.law.cornell.edu/lsrp_papers/114Garcia, R. J., Shaw, E. V., & Scurich, N. (2021). Normative and informational influence in group decision making: Effects of majority opinion and anonymity on voting behavior and belief change. Group Dynamics: Theory, Research, and Practice, 25(4), 319–333. https://doi.org/10.1037/gdn0000145Jaeger, C. B., & Trueblood, J. S. (2022). Thinking Quantum: A New Perspective on Decisionmaking in Law. Florida State University Law Review, 46(4). https://ir.law.fsu.edu/lr/vol46/iss4/1Wojciechowski, B. W., White, L. C., Allefeld, C., & Pothos, E. M. (2025). Order effects and the evaluation bias in legal decision making. Decision, 12(3), 246–267. https://doi.org/10.1037/dec0000263Wojciechowski, B. W., & Pothos, E. M. (2018). Is There a Conjunction Fallacy in Legal Probabilistic Decision Making? Frontiers in Psychology, 9, 391. https://doi.org/10.3389/fpsyg.2018.00391Wojciechowski, B. W. Quantum Probability Theory, Psychology and Law. https://doi.org/10.4324/9781003402480 =================================Secondary Sources:Boston.com — Verdict watch, deadlock noteABC7 — Day 23 deliberations, postpartum psychosis defenseNPR — Mistrial declared, 11-1 splitBoston.com — Jan. 24 timelinePlymouth County DA — Arraignment press releaseCrime Timelines — Detailed case timelineNPR — Mistrial declared, holdout juror bias claimJURIST — Legal commentary on the deadlockNBC News — Tuey-Rodriguez instructions, deadlock notes timelineCNN — Mistrial declared, SJC appeal denied
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    27 min
  • E16: How Much Should You Trust AI? – Deaths, Diagnoses, & AI Girlfriends

    E16: How Much Should You Trust AI? – Deaths, Diagnoses, & AI Girlfriends
    Sep 8 2026
    By late 2025, one in five American adults had used a chatbot as a romantic partner, and AI companion apps grew 700%+ since 2022 – but the heaviest users had the worst outcomes. That same overtrust turns lethal in a wave of lawsuits against chatbot makers: an unstopped 200-message suicide conversation with a California teen, a Texas case alleging a chatbot deepened paranoid delusions, an FSU shooter who discussed violence with a bot for over a year. Bloomberg has tracked 40+ such lawsuits since 2024. Yet two women used the same technology, with far narrower questions, to get ChatGPT to flag Hashimoto's disease — catching thyroid cancer their doctors had missed.=================================Links: 📖Substack: https://superposeddecisions.substack.com/ 📺YouTube: @Superposed_Decisions 🤝LinkedIn: https://www.linkedin.com/in/aidan-m-lewis/=================================Through quantum cognition's four pillars: trust in AI is a superposition, not a switch, oscillating between full trust and none. Interference effects – openness, loneliness – pull that wave toward trusting a chatbot that's never tired of you. Contextuality does the rest externally: no therapist, no doctor's appointment, a culture that reveres or reviles AI. And a non-commutative measurement effect quietly narrows the conversation into an echo chamber you build yourself, one engaged reply at a time. The throughline: AI validates whatever context you hand it. This is collaboration, not replacement – the decision stays humanity's only real power.=================================What you'll take away:Why trust in AI behaves as a continuous superposition rather than a binary switch, and what actually shifts the wave toward more or less trustWhy the same context-setting mechanic that helped a mother catch her own cancer also helped isolate a teenager from anyone who could have stopped himWhy the real variable that determines whether AI helps or harms someone was never the model – it's who's controlling the narrative in the chat=================================Primary Sources:Epping, G. P., Caplin, A., Duhaime, E., Holmes, W. R., Martin, D., & Trueblood, J. S. (2026). Harnessing Human Uncertainty to Train More Accurate and Aligned AI Systems. Decision Analysis. https://doi.org/10.1287/deca.2025.0395 Trueblood, J. S., Eichbaum, Q., Seegmiller, A. C., Stratton, C., O'Daniels, P., & Holmes, W. R. (2021). Disentangling Prevalence Induced Biases in Medical Image Decision-Making. Cognition, 212, 104713. https://doi.org/10.1016/j.cognition.2021.104713 Trueblood, J. S., Holmes, W. R., Seegmiller, A. C., Douds, J., Compton, M., Szentirmai, E., Woodruff, M., Huang, W., Stratton, C., & Eichbaum, Q. (2018). The Impact of Speed and Bias on the Cognitive Processes of Experts and Novices in Medical Image Decision-Making. Cognitive Research: Principles and Implications, 3, 28. https://doi.org/10.1186/s41235-018-0119-2 Humr, S., Canan, M., & Demir, M. (2025). A Quantum Probability Approach to Improving Human–AI Decision Making. Entropy, 27(2), 152. https://doi.org/10.3390/e27020152 Kvam, P. D., Busemeyer, J. R., & Pleskac, T. J. (2021). Temporal oscillations in preference strength provide evidence for an open system model of constructed preference. Scientific Reports, 11(1), 8169. https://doi.org/10.1038/s41598-021-87659-0 =================================Secondary Sources:APA on AI companion app growth and youth risk surveyPsychiatric Times on AI romantic relationshipsMIT Media Lab profile of AI relationship usersNolo on 2026 chatbot suicide/violence lawsuitsBloomberg on ~40 tracked chatbot harm lawsuitsTorHoerman Law on FSU shooting survivor's OpenAI lawsuitConsumerNotice.org on chatbot self-harm lawsuitsSokolove Law on Character.AI/Google January 2026 settlementsCallfob 2026 chatbot lawsuit legal overviewMalay Mail on Adam Raine's death and OpenAI lawsuitCBS News on Zane Shamblin ChatGPT lawsuitFox News on Lauren Bannon's ChatGPT cancer diagnosis
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    34 min
  • E15: The Cost of Deciding Too Fast – Dr. Peter Kvam on How Humans (and AI) Rush Judgments

    E15: The Cost of Deciding Too Fast – Dr. Peter Kvam on How Humans (and AI) Rush Judgments
    Sep 1 2026

    We're wired to want closure. The moment information tips us far enough in one direction, we stop gathering more and call it a decision – and that instinct, argues Ohio State decision scientist Dr. Peter Kvam, is quietly costing us better judgment, in politics, relationships, and now in AI systems too.

    Dr. Kvam – head of Ohio State University’s Decision Sciences Collaborative – walks through why rushing to commit makes opinions more extreme, not more accurate: once you decide, you stop searching for information that might complicate the picture, and the "loudest" information (the most emotionally charged, most polarizing) is what gets you there fastest. We trace how that same rush-to-judgment pattern shows up when radiologists lean too hard on AI summaries, when a cancer-detection model quietly learned to read a hospital's watermark instead of the actual pathology, and when any of us fill out a spreadsheet to "decide" a job offer we'd already picked in our gut. Along the way, Kvam offers a genuinely useful reframe – rate the quality of your options instead of forcing a binary choice – and explains, in plain language, why "measuring" a belief (i.e. making someone decide) is mathematically similar to what happens when you measure a quantum particle.

    =================================

    Links:

    📖Substack: https://superposeddecisions.substack.com/

    📺YouTube: @Superposed_Decisions

    🤝LinkedIn: https://www.linkedin.com/in/aidan-m-lewis/

    =================================

    What you'll take away:

    • Why the fastest, most confident decisions are usually the least informed ones – and why "sitting in uncertainty" is a decision-making skill, not a weakness

    • Why AI's most dangerous errors don't look like mistakes – they look like a cancer-detection model that's "right" for a reason that has nothing to do with cancer

    • Why simply reframing a decision as "rate this option's quality" instead of "should I choose this" measurably reduces polarization and extremism

    =================================

    Primary Sources

    Kvam, P. D., Sokratous, K., Fitch, A., & Hintze, A. (2024). Using artificial intelligence to fit, compare, evaluate, and discover computational models of decision behavior. Decision, 11(4), 599–618. https://doi.org/10.1037/dec0000237

    Kvam, P. D., Marley, A. A. J., & Heathcote, A. (2023). A unified theory of discrete and continuous responding. Psychological Review, 130(2), 368–400. https://doi.org/10.1037/rev0000378

    Kvam, P. D., Alaukik, A., Mims, C. E., Martemyanova, A., & Baldwin, M. (2022). Rational inference strategies and the genesis of polarization and extremism. Scientific Reports, 12, 7344. https://doi.org/10.1038/s41598-022-11389-0

    Konstantina Sokratous, Anderson K. Fitch, Peter D. Kvam,

    How to ask twenty questions and win: Machine learning tools for assessing preferences from small samples of willingness-to-pay prices, Journal of Choice Modelling, 48, 2023, https://doi.org/10.1016/j.jocm.2023.100418

    P.D. Kvam,T.J. Pleskac,S. Yu, & J.R. Busemeyer, Interference effects of choice on confidence: Quantum characteristics of evidence accumulation, Proc. Natl. Acad. Sci. U.S.A. 112 (34) 10645-10650, https://doi.org/10.1073/pnas.1500688112 (2015)

    =================================

    Secondary Sources:

    Cognition & Decision Modeling Laboratory, Dr. Peter Kvam (Ohio State University): https://peterkvam.com/research.html

    The Ohio State University Decision Sciences Collaborative: https://decisionsciences.osu.edu/

    Dr. Peter Kvam Faculty Page: https://psychology.osu.edu/people/kvam.4

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