
1. Challenges
1.1. What barriers exist to implementing reasoning training at scale?
1.2. How resistant are analysts to formal logic training?
1.3. How could organisations balance training costs and forecasting improvements?
1.4. What are the challenges in teaching these types of skills?
2. Outcomes
2.1. Can formal reasoing help analysts in high-pressure situations?
2.2. How does performance (forecast accuracy) compare with untrained analysts?
2.3. How can symbolic reasoning improve structured evaluation models?
2.4. What KPIS define forecasting success in intelligence?
2.4.1. How is success currently assessed?
2.4.2. What tools can be used to measure success?
2.4.3. What existing interventions lead to better forecasting calibration?
2.4.3.1. Can confidence and accuracey be adjusted or trained systematically?
2.4.4. Are Brier scores appropriate and valid here?
2.4.4.1. Can symbolic reasoning improve Brier scores?
2.4.4.2. Is there historical data on improvement of Brier scores?
2.4.4.3. Do Brier score improvement translate to real world success?
2.4.5. Calibration curves?
3. Temporality
3.1. Do analysts retain symbolic reasoning skills over time?
3.1.1. otherwise, what training is needed to sustain performance?
3.2. What long term direct neurological or cognitive benefits result from symbolic reasoning training?
3.2.1. Can training impede cognitive decline in anaylsts?
3.3. Are there measurable changes in analysis metrics? (cognitive flexibility, adaptability)
3.3.1. Does training impact collaborative decision-making?
3.4. Can benefits extend beyond intelligence analysis?
3.4.1. to business?
3.4.2. to law enforcement?
3.4.3. to national security?
3.4.4. to cybersecurity?
4. Process
4.1. Propositional Calculus
4.1.1. How can it help structure intelligence assessments?
4.1.2. Can it improve scenario analysis and hypothesis generation?
4.1.3. How do analysts apply logical operators (AND, OR, NOT) to intelligence judgements?
4.1.4. What cognitive benefits emerge from systemic application of logical rules?
4.2. How can Bayesian reasoning and Fermi logic improve probability estimation?
4.3. How do fuzzy logic and decision trees assist intelligence analysis?
4.4. Can AI-powered reasoning models be incorporated into analysis?
4.5. Biases
4.5.1. How can reasoning training help analysts recognize and challenge biases?
4.5.2. What techniques in intelligence are most effective for mitigating biases?
4.5.2.1. What methods best promote systematic assessment of contrary evidence?
4.5.2.2. What cognitive mechanisms cause analysts to rely on first impressions?
4.5.2.3. What cognitive mechanisms enable distinguishing between signals and noise?
4.5.2.3.1. How are these taught?
4.5.2.3.2. How are these measured?
4.5.3. How does reasoning help analysts challenge existing beliefs?
4.5.4. Overconfidence Confirmation Anchoring Satisficing
4.5.4.1. Can these be measured and reduced by training?
4.6. Structured Analytic Techniques
4.6.1. How are SATs different from symbolic logic?
4.6.2. How can SATs complement symbolic reasoning?