Applied Decision Science

Explore how humans and AI collaborate to make final decisions through human in the loop and OCI, with examples from tennis Hawk-Eye and large language models.

Explore how the economics of artificial intelligence shapes decision making, including substitution and complementarity between AI agents and human validation in a deflationary pricing environment and social considerations.

Explore the qualitative taxonomy of decisions and the main frameworks—one-way two-way doors, Eisenhower matrix, and multi-armed bandit—to distinguish reversible versus irreversible choices and exploration versus exploitation.

Learn the four pillars of decision making—outcome mapping, prior setting, actions, and reward—and how closing the cycle with counterfactuals and experiments yields true insight.

Examine the four horsemen of modern decision making: incomplete information, perverse incentives, rooted ideologies, and cognitive biases, and how they drive suboptimal choices under uncertainty.

Explore how cognitive biases shape decision making and produce suboptimal outcomes. Understand rationality as a two-part framework—decisions based on evidence and reason, and shaping the world through goals and values.

Balance reflexive (system one) and reflective (system two) reasoning to optimize decisions in uncertain and quantitative environments, using heuristics for quick actions and data-driven analysis for risk.

Contrast reflective with reflexive reasoning by showing how management uses tools like sensitivity analysis, Monte Carlo simulation, Bayesian modeling, machine learning, and heuristics to navigate risk and reward under uncertainty.

Explore how reflexive reasoning and overreliance on heuristics shape decisions, define cognitive biases as systemic deviation from rationality, and examine biases like confirmation, overconfidence, and anchoring to improve judgment.

Take the CBI test to generate your personalized profile of cognitive biases, compare it with the population, and learn how these biases affect decision making and how to improve them.

Explore how poker, entrepreneurs, and financial investors navigate decision making under uncertainty by setting priors and ranges, choosing actions, and overcoming biases like sunk cost, confirmation, status quo, and groupthink.

Apply reflexive reasoning to navigate cognitive biases in decisions under uncertainty. Master four frameworks—first principle thinking, triangulation thinking, probabilistic thinking, and error type thinking—to generalize tools for practical rational thinking.

Apply first principle thinking to humor by deconstructing why jokes land, examining subjectivity and attributes like timeliness, incongruity, and relatability, then reconstruct a principle of benign violation for funny material.

Explore triangulation thinking to overcome social biases, leveraging collective intelligence to refine initial perspectives toward the middle ground.

Probabilistic thinking introduces a multi-step decision framework that weights outcomes, plans contingencies for high and low probability events, and weighs one-way versus two-way doors in future decisions.

Apply error type thinking to balance reflective reasoning and reduce biases in decision making, prioritizing quality by avoiding type one errors (false positives) and tolerating type two errors (false negatives).

Apply four methodologies to shift from reflexive reasoning to more robust decision-making for We Sell things.com, a scale-up under uncertainty, using heuristics and system thinking to manage cognitive biases.

Strategize a defensible digital transformation by evaluating fraud modeling, pricing, personalization, and product portfolio streams, balancing value, risk, and complexity to select a PoC priority.