March Erb Scott E Page University of Michigan and Santa Fe Institute Complex Systems, Political Science, Economics

March Erb Agent Based Modeling The Interest in Between

March Erb Outline What it is? A ladder of models A core question The in between Four uses

March Erb What is it?

March Erb The Spherical Cow

March Erb A Whole Lotta Spherical Cows

March Erb A New Kind of Science Stephen Wolfram

March Erb Wolfram’s 256 Automata N X

March Erb Rule 90 N X Sum = 90

March Erb Wolfram’s Findings Simple rules can create patterns like those in nature randomness computation Summary: `it from bit’

March Erb Conway’s Game of Life X Cell has eight neighbors Cell can be alive Cell can be dead Dead cell with 3 neighbors comes to life Live cell with 2,3 stays alive

March Erb Examples X

March Erb A ladder of models

March Erb Gell Mann’s Version ``Imagine how hard physics would be if electrons could think.”

March Erb Model as Metaphor Forest Fires & Bank Failures

March Erb Forest Fire Model At each site tree grows with prob p Trees are good, lightening hits w/ prob q Fires spread to neighboring trees

March Erb Bank Failure Model Make risky loans each period with prob p Risky loans fail with prob q, but pay more Failures spread to neighboring banks

March Erb Example Period 1: OOROOROOORROR Period 2: ROROOROORRRRR

March Erb Example Period 1: OOROOROOORROR Period 2: ROROOROORRRRR Period 3: ROROOROOFRRRR Period 4: ROROOROOFFFFFF Period 5: ROROOROOOOOOR

March Erb The Bottom Rung: Rule Aggregation

March Erb A Phase Transition rate of risky loans yield

March Erb The Second Rung: Global Selection

March Erb The ‘edge of chaos’ p* yield

March Erb The Third Rung: Individual Adaptation

March Erb What’s the matter here? p* yield

March Erb Emergence of Firewalls 111O11O111O1111OO111

March Erb The Top Rung: Optimal behavior

March Erb The Optimal Solution

March Erb We follow routines We select better rules We respond and learn We have it all figured out

March Erb We follow routines: laundry We select better rules: where we shop We respond and learn: dating We have it all figured out: tic tac toe

March Erb A core question

March Erb ``What happens once we define the set of the possible and the rules of the game?’’

March Erb Though policy analysis focuses on what happens if, we must also consider what happens if not.

March Erb What goes up….

March Erb Must come down.

March Erb The Business Environment Incentives: unfettered and induced Regulations and restrictions Technological change Information Global climate change Demographic and preference change

March Erb The in between

March Erb How we answer the core question Thick description (TD) Simple models (SM)

March Erb Agent based models enable us to explore the space in between the incredibly rich and complex real world and our stark models. We can explore the attainability of outcomes, the robustness of functionalities, and the path dependence of systems.

March Erb ABM can easily (and poorly) include heterogeneity networks and space adaptation feedbacks and lags

March Erb Flexibility Logical Consistency TD ABM SM

March Erb Four Uses

March Erb ABM models complement SIR(S) models by including social networks, transportation systems, and agent level heterogeneity (genotypic and phenotypic) and adaptive responses Math +

March Erb ABM models allow us to test the implications of policies. Project SLUCE considered effects of sprawl policies on ecosystems at the exurban fringe. The laboratory

March Erb ABM models can be used as test beds for experiments with real people. Differences often minor -- TFT emerged in first experiments with both people and artificial agents. The people alternative

March Erb ABM models can be used to explore the implications of assumptions. From them we’ve learned how birds flock, how patterns form, and why some communicable diseases have waves. The intuition builder

March Erb Not if ABM, but how? The economics of methodology

March Erb This won’t happen by chance