Richards J. Heuer, Jr.
This volume pulls together and republishes, with some editing, updating, and additions, articles written during 1978-86 for internal use within the CIA Directorate of Intelligence. Four of the articles also appeared in the Intelligence Community journal Studies in Intelligence during that time frame. The information is relatively timeless and still relevant to the never-ending quest for better analysis.
Note by Alex: highly valuable research on cognitive biases useful for making risk-based decisions and risk management.
Prepared by the US Government
This primer highlights structured analytic techniques—some widely used in the private
sector and academia, some unique to the intelligence profession. It is not a
comprehensive overview of how intelligence officers conduct analysis. Rather, the
primer highlights how structured analytic techniques can help one challenge judgments,
identify mental mindsets, stimulate creativity, and manage uncertainty. In short,
incorporating regular use of techniques such as these can enable one to structure
thinking for wrestling with difficult questions.
Note by Alex: highly valuable research on decision making and risk management, some of the easiest and effective tools for risk-based decision making.
The purpose of this handbook is to provide guidance for implementing the Risk Management requirements, with a specific focus on programs and projects, and applying to each level of the NASA organizational hierarchy as requirements flow down.
This handbook supports RM application within the NASA systems engineering process, and is a complement to the guidance contained in NASA/SP-2007-6105, NASA Systems Engineering Handbook. Scope and Depth This handbook provides guidance for conducting RM in the context of NASA program and project life cycles, which produce derived requirements in accordance with existing systems engineering practices that flow down through the NASA organizational hierarchy. The handbook highlights major issues to consider when managing programs and projects in the presence of potentially significant uncertainty, so that the user is better able to recognize and avoid pitfalls that might otherwise be experienced.
Note by Alex: great example of having risk management 1 and risk management 2 and applying proper quantitative decision making and risk analysis tools.
Risk management is ultimately about creating a culture that would facilitate risk discussion when performing business activities or making any strategic, investment or project decision. In this free book, Alex Sidorenko and Elena Demidenko talk about practical steps risk managers can take to integrate risk management into decision making and core business processes. Based on our research and the interviews, we have summarised fifteen practical ideas on how to improve the integration of risk management into the daily life of the organisation. These were grouped into three high level objectives: drive risk culture, help integrate risk management into business and become a trusted advisor. This document is designed to be a practical implementation guide. Each section is accompanied by checklists, video references, useful links and templates. This guide isn’t about “classical” risk management with its useless risk maps, risk registers, risk owners or risk mitigation plans. This guide is about implementing the most current risk analysis research into the business processes, decision making and the overall culture of the organization.
Note by Alex: ok, this book is ours and true, it is number 1 in Google search
Youtube has great risk management resources:
- RISK-ACADEMY channel https://www.youtube.com/channel/UCog9jkDZdiRps2w27MZ5Azg
- Risk bites https://www.youtube.com/user/riskbites
ModelRisk is a Monte Carlo simulation Excel add-in that allows the user to include uncertainty in their spreadsheet models. ModelRisk has been the innovation leader in the marketplace since 2009, being the first to introduce many technical Monte Carlo method features that make risk models easier to build, easier to audit and test, and more precisely match the problems you face.
A ModelRisk user replaces uncertain values within their Excel model with special ModelRisk quantitative probability distribution functions that describe the uncertainty about those values. ModelRisk then uses Monte Carlo simulation to automatically generate thousands of possible scenarios.
Note by Alex: it’s like Palisade @Risk but stabler, quicker and free.
Another Excel add-on developed by Sam Savage using the Stochastic Information Packet (SIP) represents a probability or frequency distribution as a data structure that holds an array of values and some metadata. The open SIPmath™ Standard enables legacy and future simulation models to communicate with each other, ushering in a new paradigm for enterprise risk management. SIPs advance the modeling of uncertainty in four fundamental ways. SIPs are:
Note by Alex: great tool for adding Monte-Carlo functionality to Excel while making sure all simulations are traceable and auditable. Great tool.
What other free resources would you add?