Ways to Organize the Course

Because the course covers so much ground, with all the topics interrelated in different ways, there are multiple sequences that can work well. Different faculty may wish to teach the course in their own sequence. Moreover, sometimes it makes more sense to alternate between themes in order to facilitate students’ ability to integrate them into a conceptually coherent approach to problem-solving.

Below, we offer two possible ways to organize the course conceptually, the first based on theme, the second on motivating questions.

These are of course by no means exhaustive. If you have an idea about another approach to structuring this material that has other affordances, we’d love to hear about it!

Original Organization Based on Themes:

  1. Philosophical Underpinnings: Models, Causation, and Reality.  #org
    1. Importance to society: Decisions and planning need science #FV
    2. Belief in a common, shared objective reality.   #S
    3. Causal reasoning: correlation vs causation.  #CC
    4. “Singular causation” vs. “general causation.”  #SG
    5. Science isn’t just reductionism: emergent phenomena, complexity. #EP
  2. Probabalistic/Statistical thinking.  #org
    1. Tentative propositions.   #CR
    2. Calibration of credence levels can be trained.  #CL
    3. Finding signal in noise.   #SN
    4. Seeing patterns in random noise.   #PN
    5. Statistical vs. systematic uncertainties.   #SU
  3. “Can-do” aspect of science.  #org
    1. Scientific optimism and iteration    #SO
    2. Orders of understanding and a parseable world.  #OU
    3. Fermi problems.    #FP
  4. Human cognitive failings.  #org
    1. Availability, representativeness, anchoring heuristics.   #HB
    2. Confirmatory biases.  #CB
    3. Blind analysis   #BA
  5. Group thinking.  #org
    1. Wisdom of crowds vs. herd thinking.   #WC
    2. Optimal/non-optimal approaches to group decisions.   #WC
    3. How should/shouldn’t values, emotions, goals, and conflicts of interest be woven into decision making.   #FV  #DP  #BT
  6. When is science suspect?  #org
    1. “Mis-measure of Man”  (How scientific approaches can go ethically awry) #MM
    2. Pathological science   #PS
    3. When is a scientific approach not appropriate?   #NA

Organization of Course Based on Motivating Questions:       #mq #org

  1. Groundwork: Why is science effective?
    1. Shared Reality #SR #org
    2. Senses & Instrumentation #SI #org
    3. Scientific Optimism #SO #org
  2. Methods: How do we find out how things work?
    1. Conceptual & Empirical Issues in Understanding Causation:
    2. Correlation & Causation #CC #org
    3. Causality in the Messy Real World #CW #org
    4. Singular vs. General Causation #SG #org
    5. Orders of Understanding #OU #org
  3. Cognitive Humility: What do we know?
    1. Signal & Noise #SN #org
    2. Seeing Patterns in Noise #PN #org
    3. Systematic & Statistical Uncertainty #SU #org
    4. Credence Levels #CR #org
    5. Calibration of Credence Levels #CL #org
  4. Strategies: How can we avoid going wrong?
    1. Types of Errors & Their Costs #TE #org
    2. Heuristics & Biases #HB #org
    3. Confirmation Bias #CB #org
    4. Blind Analysis #BA #org
    5. Mismeasure of Man #MM
    6. Pathological Science #PS #org
    7. Fermi Estimates #FE #org
  5. Applications: How can we make better decisions?
    1. Decisions and Planning Need Science #FV #org
    2. Integrating Facts & Values #IN #org
    3. Wisdom of Crowds vs. Herd Thinking #WC #org
    4. Denver Bullet Study #DB #org
    5. Deliberative Polling #DP #org
    6. Scenario Planning #SP #org
    7. Can We Do Better Together? #BT #org