Upload 4 module txt files

modules/module01.txt

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+MODULE NAME:
+Module 01 – Philosophy, Logic, and Intro to Ethics/ESG
+
+LEARNING OBJECTIVES:
+- Identify and evaluate arguments (premises and conclusions), including detection of formal and informal fallacies
+- Classify moral arguments according to four traditions: utilitarian (Bentham), rights/duties (Kant), virtue (Aristotle), care (Gilligan)
+- Understand introductory ESG considerations
+- Analyze business ethical dilemmas
+- Create your own moral arguments in favor of a business decision
+
+KEY POINTS:
+• Arguments have premises (reasons) and conclusions (claims). Valid arguments have logical structure; sound arguments are valid AND have true premises.
+• Formal fallacies: Errors in logical structure (affirming the consequent, denying the antecedent).
+• Informal fallacies: Errors in reasoning (ad hominem, straw man, false dilemma, appeal to authority, slippery slope).
+• Utilitarian ethics (Bentham/Mill): Right action = greatest good for greatest number. Focus on consequences and outcomes.
+• Rights/duties ethics (Kant): Right action = follows universal moral rules and treats people as ends, not means. Focus on principles and duties.
+• Virtue ethics (Aristotle): Right action = what a virtuous person would do. Focus on character and excellence.
+• Care ethics (Gilligan): Right action = maintains relationships and responds to needs. Focus on context and connection.
+• ESG (Environmental, Social, Governance): Framework for evaluating business decisions beyond profit - considers stakeholder impact, sustainability, fairness, and long-term value creation.

modules/module04.txt

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+MODULE NAME:
+Module 04 – Wise Decisions
+
+LEARNING OBJECTIVES:
+- Define the concepts of game theory and neo-classical rationality
+- Apply the prisoners' dilemma to your lived experience
+- Distinguish between good decisions and good outcomes
+- List the eight steps of a high-quality deliberative decision process
+- Use Ben Franklin's Pro-Con method to make an important go/no-go decision
+- Calculate the best choice using a Decision Matrix
+
+KEY POINTS:
+• Neo-classical Rationality: Making consistent, logic-based decisions to maximize utility. Assumes perfect information and logical thinking. Real people often satisfice (choose first acceptable option) due to bounded rationality.
+• Satisficing: Choosing the first option that meets an acceptable threshold rather than finding the optimal solution. Rational when information is incomplete, search costs are high, or time is limited.
+• Game Theory: Models strategic interactions where outcomes depend on multiple actors' choices. Players must anticipate others' moves.
+• Prisoner's Dilemma: When two players act selfishly, both end up worse off. Cooperation (often through reputation, rules, or trust) can improve outcomes for everyone. Common in business: price wars, resource sharing, industry standards.
+• Incentives & Conflicts of Interest: Principals (owners) and agents (managers) often have different goals. Good governance aligns incentives through monitoring, compensation, and accountability.
+• Bayes' Theorem: Formula for updating beliefs as new evidence appears. Start with base rate (prior probability), add new evidence (likelihood), calculate updated belief (posterior). Using outside view (base rates, historical data) is often more accurate than inside view (personal experience, intuition).
+• Decision Quality vs. Outcome: High-quality decision = good reasoning process, regardless of result. Outcome bias = judging decisions only by results. Chance and external factors affect outcomes, so focus on improving process quality.
+• Pro-Con Method (Ben Franklin's Moral Algebra): List pros and cons, assign weights to each, cancel equal weights, see which side is heavier. Slows impulsive choices. Only works for binary (yes/no, go/no-go) decisions.
+• Decision Matrix: Compare multiple options across weighted criteria. For each option: score it on each criterion (e.g., 1-5), multiply score by criterion weight, sum across all criteria. Higher total = better choice. Reduces bias, clarifies trade-offs, documents reasoning.
+• Eight Steps of Deliberative Decision Process: (1) Identify the problem (2) Define objectives (3) Generate alternative options (4) Gather relevant data (5) Evaluate options against objectives (6) Choose the best option (7) Implement the decision (8) Review and learn from results.
+• Mindfulness in Decisions: Being fully present, aware of thoughts/feelings without judgment. Slows automatic reactions, creates space for reflection, reduces bias, improves judgment quality.

modules/module06.txt

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+MODULE NAME:
+Module 06 – Decisions Under Risk
+
+LEARNING OBJECTIVES:
+- Calculate the expected value of a choice with two or more outcomes
+- Summarize the process and applications of decision trees to decision-making
+- Sketch and interpret a decision tree
+- Describe the process and applications of Monte Carlo simulations to decision-making
+- Create and interpret a Monte Carlo simulation of a business investment decision
+
+KEY POINTS:
+• Expected Value (EV): The rational decision rule is to choose the option with the highest positive expected value. EV is a probability-weighted average of all possible outcomes, calculated using E(x)=Σxp(x). Decision-makers can be risk averse (require payoff > EV), risk neutral (accept payoff = EV), or risk seeking (accept payoff < EV).
+• Decision Trees: Used for analyzing path-dependent decisions under uncertainty. Tree structure maps potential futures using: square nodes (choices), lines (path-dependency), circle nodes (chance events). Solve by mapping choices left to right, then calculate values right to left to find optimal (highest EV) path. Shows value of options like quit or expand. Challenge: assigning numerical values to non-quantifiable outcomes and accurately estimating probabilities.
+• Monte Carlo Simulations: Computational method that "embraces randomness" to model situations with significant uncertainty. Useful for complex systems in project management and finance. Process: (1) Replace uncertain inputs with random variables (values are random but statistical distribution is known), (2) Run simulation thousands of times, (3) Generate distribution of possible outcomes, (4) Average of outcomes = expected value. In Excel: use RANDBETWEEN(bottom,top) to add risk, create scenario outputs with data table, summarize with AVERAGE and STDEV.S functions and histogram.

modules/module08.txt

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+MODULE NAME:
+Module 08 – Optimization and Behavioral Economics
+
+LEARNING OBJECTIVES:
+1. Use Excel's Solver to find optimal solutions to linear programming problems
+2. Describe classical notions of rationality
+3. Define Behavioral Economics
+4. Explain bounded rationality and prospect theory
+5. Identify situations in which people's decisions appear to be irrational (Prospect Theory & Fairness)
+
+KEY POINTS:
+
+**Optimization with Excel Solver:**
+- The LP Framework has three components: Decision variables (what to solve for), Constraints (subject to limitations), and Objective function (what to maximize or minimize)
+- Excel Solver workflow: 1) Apply LP Framework, 2) Create Excel Model (set cells for decision variables, calculate objective, set cells for constraints), 3) Populate & Run Solver
+- Solver is found in Excel under Data tab (must activate add-in first via File>Options>AddIns)
+
+**Classical Rationality:**
+- "Homo Economicus": hypothetical person who behaves in exact accordance with rational self-interest
+- Neo-classical rationality's four pillars: self-interest, omniscience (complete information), deliberation, and optimization
+- Classical model assumes humans: know and never change preferences, have no cognitive limitations, possess complete information, face no time constraints, maintain perfect self-control
+
+**Behavioral Economics:**
+- Definition: Method of economic analysis that applies psychological insights into human behavior to explain economic decision making
+- Real-world applications: Marketing (fast food menu design), Risk Management, Organizational Behavior, Strategic Decision-Making (targeted social media ads)
+
+**Bounded Rationality (Herbert Simon):**
+- Contrasts "satisficing" vs "optimizing" behavior
+- The effort-quality curve shows bounded rationality produces an S-curve while unbounded rationality assumes linear improvement
+- Practical examples: Starbucks menu simplification, HBO Max streaming service design
+
+**Prospect Theory (Kahneman & Tversky):**
+- Value is measured relative to a reference point, not absolutely
+- Diminishing sensitivity example: difference between $100-200 feels larger than difference between $900-1000
+- Loss aversion leads to rejecting positive expected value investments
+
+**Fairness:**
+- Ultimatum Game results: offers typically cluster around 40-50% splits; offers below ~40% are frequently rejected
+- Fairness observed cross-species (demonstrated in primate experiments)
+- Video reference: monkey fairness experiment showing cucumber vs grape reactions
+
+**System 1 and System 2 Thinking:**
+- System 1 brain regions: Superior medial frontal/anterior cingulate, posterior cingulate/precuneus, bilateral angular gyri
+- System 2 brain regions: Bilateral middle frontal region of lateral prefrontal cortex
+- Bat and ball problem: System 1 suggests "10 cents" but correct answer is 5 cents (demonstrates intuitive override)
+- Linda Problem: demonstrates representativeness heuristic overriding probability logic
+
+**The Deliberative Decision Process (where optimization fits):**
+- Steps: Frame decision → Gather information → Identify stakeholders → Prioritize criteria (KPIs) → Generate alternatives → Analyze with decision tool → Synthesize → Execute → Measure → Learn
+- Optimization is one of several analytical tools (alongside Pro/Con, Matrix, NPV/IRR, Decision Tree, Monte Carlo, Linear Programming)