MINDSET AND COGNITIVE PERFORMANCE

The Certificate is designed to strengthen the higher-order cognitive competencies that human judgment requires when decisions are increasingly co-produced with algorithmic systems. Rather than teaching participants to operate specific AI tools, the Certificate cultivates the reasoning, self-awareness, and critical discernment needed to remain an autonomous and accountable decision-maker in political and managerial environments saturated by data-driven recommendation, prediction, and automation. The program pursues three integrated aims. First, it develops metacognitive and critical-thinking competencies (Life Skills): the ability to recognize one's own cognitive biases, to calibrate confidence, to interrogate assumptions, and to sustain deliberate reasoning where automated systems invite cognitive offloading. Second, it builds analytical and technological literacy (STEM Readiness): a working understanding of how machine-learning systems, choice architectures, and recommendation engines shape the information environment, interact with human heuristics, and can both amplify and mitigate bias. Third, it strengthens competencies oriented toward professional judgment and responsible decision-making (Career Path): the capacity to design human–AI decision processes that preserve autonomy, protect pluralism, and withstand scrutiny in organizations and public institutions. At the end of the program, students will have acquired competencies useful for exercising independent critical judgment, for diagnosing where algorithmic mediation threatens or supports sound decision-making, and for safeguarding cognitive autonomy and viewpoint diversity within the political and managerial settings in which they will operate.

No formal technical background is required. The Certificate is designed for master's students across management, political science, and international affairs, and assumes no prior programming or data-science expertise. A basic familiarity with common generative-AI assistants (such as conversational chatbots) and with everyday digital platforms is helpful, since the sessions draw on participants' own experience of these tools. An openness to reflective, discussion-based learning and a willingness to examine one's own reasoning and decision habits will allow students to benefit fully from the planned activities. These indications are non-binding and are intended to support an informed choice of the program.

Upon completion of the Certificate, students will be able to: • analyze the principal cognitive biases and heuristics that affect individual and collective judgment, and identify how algorithmic systems interact with, exploit, or amplify them; • evaluate the reliability and limits of AI-generated recommendations, distinguishing tasks in which automation supports judgment from those in which it induces automation bias, complacency, or skill atrophy; • apply metacognitive strategies – confidence calibration, structured questioning, and critical ignoring – to sustain deliberate reasoning when working with AI systems; • interpret the effects of choice architectures, recommendation engines, and personalization on personal autonomy, epistemic agency, and democratic discourse; • assess threats to pluralism such as echo chambers, filter bubbles, group polarization, and elite narrowing of perspective, and design conditions that protect cognitive diversity, explicit trade-off awareness, and viewpoint contestability in teams and institutions; • develop human–AI decision processes for political and managerial contexts that preserve human oversight, accountability, and legitimacy. The competencies developed are particularly relevant for professional roles in strategy and general management, organizational decision-making, public administration, policy analysis, international affairs, government relations, and public-interest advocacy, where the quality, autonomy, and accountability of human judgment remain decisive even as AI systems become pervasive.

The Certificate addresses the cognitive foundations of sound judgment and the ways in which artificial intelligence is reshaping them in the domains of politics and management. Moving from individual cognition to collective decision-making, the program explores how human reasoning, algorithmic mediation, autonomy, and pluralism interact, and how decision-makers can respond deliberately rather than by default. The main contents include: theoretical frameworks of judgment and decision-making: bounded rationality, dual-process reasoning, heuristics, and cognitive biases; the interaction between human biases and algorithmic systems, including algorithmic bias, recommendation and personalization, and the amplification of distortion; automation bias, complacency, cognitive offloading, and the empirical evidence on AI's effects on critical thinking and skill retention; metacognition and practical strategies for critical, calibrated engagement with AI tools; autonomy and manipulation in digital environments: choice architecture, behavioral prediction, and epistemic agency; pluralism, polarization, echo chambers, and cognitive diversity as epistemic and democratic assets, with special attention to the distinction between ends and means, the false neutrality of apparently objective solutions, and the risk of elite narrowing of perspective; human–AI decision-making in organizations and public institutions: the automation–augmentation paradox, algorithmic management, oversight, and accountability; applied cases drawn from managerial, organizational, and political-institutional contexts.

Reference materials are organized into essential readings, required to follow the activities, and supplementary readings for individual deepening. Specific chapters and articles are assigned session by session (see the extended weekly program). Essential readings: Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. Gigerenzer, G. (2022). How to Stay Smart in a Smart World: Why Human Intelligence Still Beats Algorithms. MIT Press. Kozyreva, A., Lewandowsky, S., & Hertwig, R. (2020). Citizens Versus the Internet: Confronting Digital Challenges with Cognitive Tools. Psychological Science in the Public Interest, 21(3), 103–156. Parasuraman, R., & Manzey, D. H. (2010). Complacency and Bias in Human Use of Automation: An Attentional Integration. Human Factors, 52(3), 381–410. Raisch, S., & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation–Augmentation Paradox. Academy of Management Review, 46(1), 192–210. Supplementary readings: Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124–1131. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press. Susser, D., Roessler, B., & Nissenbaum, H. (2019). Online Manipulation: Hidden Influences in a Digital World. Georgetown Law Technology Review, 4(1), 1–45. Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs. Nguyen, C. T. (2020). Echo Chambers and Epistemic Bubbles. Episteme, 17(2), 141–161. Sunstein, C. R. (2017). #Republic: Divided Democracy in the Age of Social Media. Princeton University Press. Landemore, H. (2013). Democratic Reason: Politics, Collective Intelligence, and the Rule of the Many. Princeton University Press. O'Neil, C. (2016). Weapons of Math Destruction. Crown. Lorenz-Spreen, P., Lewandowsky, S., Sunstein, C. R., & Hertwig, R. (2020). How behavioural sciences can promote truth, autonomy and democratic discourse online. Nature Human Behaviour, 4, 1102–1109. Lee, H.-P., Sarkar, A., Tankelevitch, L., et al. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems.

The Certificate adopts active, experiential, and competency-based methods aligned with its objectives. Because the program's aim is to strengthen judgment rather than to transmit tool-specific procedures, every session combines conceptual input with reflective practice on participants' own reasoning and decisions. Methods include: Experiential and collaborative learning: group projects, simulations, role-plays, and mini-challenges based on real political and managerial cases, think-pair-share, and peer feedback. Project- and data-based learning: analysis of algorithmic environments and decision workflows, thematic deep-dives, and the redesign of biased decision processes. Guided discussions and debates: Socratic seminars, structured debates that steel-man opposing views, and expert-led discussion. Targeted, reflective use of AI: AI assistants used both as objects of critical analysis and as instruments to be audited – generating first-pass analyses, diverse perspectives, and problem-solving support that participants then interrogate for bias, reliability, and homogenization.

Assessment is continuous and competency-based, combining individual and group components. It rewards demonstrated critical judgment, metacognitive awareness, and the ability to apply the program's frameworks to real political and managerial decisions rather than the memorization of content. Students will be evaluated through: (1) weekly individual deliverables produced during the six sessions – short reflections, a bias audit, a critical-engagement protocol, an analytical note, and a pluralism proposal – which together document individual progress; (2) a collaborative in-session component, including participation in structured debates, simulations, and peer feedback; and (3) a final individual applied project, namely a policy memo or managerial decision analysis in which the student diagnoses the cognitive and algorithmic risks of a real decision and designs a human-in-command process to address them, presented orally with a Q&A session. Indicative weighting: weekly deliverables and in-session participation, 50%; final applied project and its oral defense, 50%. The overall assessment is expressed on a pass/fail basis. Requirement for obtaining the Certificate: regular attendance of at least 70% of the learning activities and successful completion of the final assessment. For non-compliant students and students exempt from the attendance requirement, learning outcomes will be assessed through an oral examination, defined by the instructor in accordance with the Certificate content, learning objectives, and expected learning outcomes. For this purpose, the examination for non-compliant students and students exempt from the attendance requirement may include, at the instructor's discretion, questions on course content, discussion of cases or practical scenarios, analysis and discussion of materials, presentation and discussion of individual assignments, or any other assessment method deemed appropriate to verify the full achievement of the expected learning outcomes. The Digital Badge will only be awarded to students who meet the minimum 70% attendance requirement. Therefore, students who, for any reason, including exemption from the attendance requirement, do not meet this threshold will not be eligible to receive the Digital Badge, even if they successfully pass the final examination.

Not applicable