Project Management›Project Risk Management›Series
Algorithms for Decision Making
A risk-focused field guide to deciding well under uncertainty — twenty-six chapters across five parts, re-read for the project professional rather than the roboticist.
The premise
Every risk decision is made without full knowledge
Risk management is decision-making under uncertainty. That is its entire job.
This series works through the modern computational toolkit built for exactly that problem: how a rational actor should choose when outcomes are probabilistic, information is incomplete, and the situation unfolds over time. The mathematics comes from artificial intelligence and operations research — but the reading here is deliberately practical. Less “how to fly an aircraft autonomously,” more “how to decide well when you cannot see the whole board.”
Each chapter explains one method in plain terms, then draws the line back to the work a project professional actually does: quantifying exposure, sequencing responses, learning from outcomes, acting on imperfect status, and coordinating with everyone else who is also making decisions.
How the series is organised
A ladder of uncertainty
Each part takes away one more thing you are allowed to know for certain, then asks how to decide anyway. Climb it and the problem gets harder — and closer to how real projects actually feel.
You know the model
Quantify belief and pick the best single choice — probabilistic reasoning and simple decisions.
You know the dynamics
Decide across a whole timeline, not one moment — Markov decision processes.
You don’t know the model
Learn the odds while you act — reinforcement learning and exploration.
You can’t see the true state
Decide on belief, not observation — partially observable problems.
Others are deciding too
Act strategically among many actors — multiagent systems.
The series
Twenty-six chapters, five parts
Every card names one method, a one-line synopsis, and its risk lens — what the idea means for real project decisions.
Probabilistic Reasoning
Quantify what you believe before you decide.
Representation
Encoding beliefs as probability distributions and Bayesian networks — a shared, explicit picture of what drives uncertainty.
Inference
Computing the probability of outcomes given the evidence you have observed.
Parameter Learning
Fitting a model’s numbers from data rather than guessing them.
Structure Learning
Discovering which factors actually influence which — learning the shape of the model.
Simple Decisions
Turning beliefs and preferences into a single best choice, and pricing the value of more information.
Sequential Problems
Decide well over a whole timeline, not one moment.
Exact Solution Methods
Optimal policies when the dynamics are known — Markov decision processes, value and policy iteration.
Approximate Value Functions
Scaling to problems too large to solve exactly by approximating the value of each state.
Online Planning
Deciding on the fly by looking ahead from where you are now — forward search and Monte Carlo tree search.
Policy Search
Searching directly for a good decision rule instead of computing values first.
Policy Gradient Estimation
Measuring in which direction a decision rule should change to do better.
Policy Gradient Optimization
Improving a decision rule stably, without lurching or over-correcting.
Actor–Critic Methods
Pairing a value estimate (the critic) with a decision rule (the actor) so each improves the other.
Policy Validation
Testing a decision policy against many scenarios before you trust it in the real world.
Model Uncertainty
Decide well even when you don’t know the odds.
Exploration & Exploitation
Balancing learning something new against acting on what you already know pays off.
Model-Based Methods
Learning a model of the world from experience, then planning against it.
Model-Free Methods
Learning good actions directly from outcomes, without ever building an explicit model.
Imitation Learning
Learning a policy by watching an expert rather than by trial and error.
State Uncertainty
Decide well when you can’t even see the true situation.
Beliefs
Tracking a probability distribution over hidden states as evidence trickles in.
Exact Belief-State Planning
Optimal decisions under partial observation — the partially observable Markov decision process.
Offline Belief-State Planning
Pre-computing a robust policy in advance so you’re ready whatever you observe.
Online Belief-State Planning
Planning under partial information in real time, from your current belief.
Controller Abstractions
Compact, executable decision policies simple enough to run and audit.
Multiagent Systems
Decide well when others are deciding too.
Multiagent Reasoning
Game-theoretic reasoning about actors whose choices affect yours.
Sequential Problems
Decisions taken over time among many interacting actors.
State Uncertainty
Partial information shared unevenly among many actors, each holding private knowledge.
Collaborative Agents
Aligning independent decision-makers toward a genuinely shared objective.
Where to start
Three ways through
You don’t have to read all twenty-six in order. Pick the route that matches the decision in front of you.
The essentials
Enough to quantify exposure and sequence decisions well. The backbone every risk practitioner should hold.
The uncertainty track
For work where the model is unknown or the true state is hidden — the hard, realistic middle of most projects.
The full path
The complete ladder, bottom to top, as the field itself builds it. Best read over time, one part per sitting.
About this series
Original synthesis, plainly stated
Every explanation, diagram, and risk lens in this series is original KEVOS® work. Nothing is reproduced verbatim. The subject matter is the established field of computational decision-making — probabilistic graphical models, Markov decision processes, reinforcement learning, partially observable problems, and multiagent systems — as consolidated in the standard graduate text by Kochenderfer, Wheeler and Wray. The value KEVOS® adds is the translation: taking a rigorous but abstract body of theory and reading it through the single question a project professional keeps asking — how do I decide well when I cannot see the whole board?
Each chapter page stands on its own, favours intuition over notation, and closes with what the method means in practice. Where theory would help but isn’t essential, it’s noted, not dumped.