https://www.coursera.org/learn/model-thinking Week 1 - Models tie us to a mast of logic - Models should be blended with experience to make decisions. - Models won't always work in all situations. Using lots of models can help inform when models are inappropriate - Step 1. Name the parts (likely relevant parts only) - Step 2. Identify relationships between parts - Models can be used to inductively explore - Models can be used to understand class of outcome (Cyclical, random, equilibrium, complex) - Models can be used to understand patterns in data or predict points, produce bounds, and inform data collection - Models can be used to identify and rank levers (imagine carbon cycle, ag is largest contributor, so it is a good lever) - Models can be (1) equation based, (2) Agent-based - models interactions of agents based on behavior rules - Week 2 - Simple, rule based models can develop into equilibrium, patterns, chaos, or complexity - Six sigma is a business process that is intended to ensure that failure only happens in 'six sigma' events, or where an event with a 3.4 in a million chance occurs. - Week 5 Markov Models - Understanding conservation using Markov models - The State and Transition Model is a Markov model for understanding ecological condition. - Markov models are models that find equilibrium based on transition probablities - Markov models will always arrive at the same equilibrium state given fixed transition probabilities - A key lesson from Markov models is that interventions that only change some states to other states (e.g., restoration) do not affect the outcome--the equilibrium will always be achieved given enough time - Interventions that only change some states to other states are sometimes useful because reaching the equilibrium may take time, and there may be some benefit achieved in the interim - Truly impactful interventions are those that change the transition probabilities - Thus, one-off restoration projects and similar efforts will only delay the inevitable - True conservation solutions must seek to change the transition probabilities - For example, sage-grouse habitat can be categorized into a number of states (A, B, C, D, E) - Given the status quo, the transition probabilities are fixed and there is a shuffling of lands between states (e.g., some percent chance of overgrazing, some percent chance of development, some percent chance of restoration). - With energy development, the transition probability from suitable to unsuitable increased - To offset this impact, a similar change in transition probability from unsuitable to suitable must be achieved - The offset must be sufficient to maintain the same equilibrium point