The CENKI Space Economic Simulator: Analytical Verification of an - - PowerPoint PPT Presentation

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The CENKI Space Economic Simulator: Analytical Verification of an - - PowerPoint PPT Presentation

The CENKI Space Economic Simulator: Analytical Verification of an Agent-Based Modeling Engine Track 10. Software and Computing Trevor Bennett 3 , Charles Cain 3 , N. S. Campbell 1 , Andrew (AJ) Gemer 2 , John Marino 1 , Tobias Niederwieser 1 ,


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The CENKI Space Economic Simulator: Analytical Verification of an Agent-Based Modeling Engine

Track 10. Software and Computing Trevor Bennett 3, Charles Cain3, N. S. Campbell1, Andrew (AJ) Gemer2, John Marino1, Tobias Niederwieser1, Akhil Rao4

1Aerospace Engineering Sciences, University of Colorado, Boulder, CO 2The Laboratory for Atmospheric and Space Physics (LASP), Boulder, CO 3CENKI Industry Member 4Department of Economics, University of Colorado, Boulder, CO

IEEE Aerospace Conference, Big Sky, MT, March 8th, 2018

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Background

CENKI

The Committee for Expansion into Key Space Industries

◮ Formed in 2016 out of a CU special topics

project with ULA

◮ Space is complex ◮ Need for community consensus

Mission Statement: CENKI will assemble the community and technical resources to stimulate the development of a thriving space economy

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The problem

◮ There is a need to model economic interactions between

actors in space industries

◮ Existing solutions can optimize logistics or behaviors; existing

approaches can model specific scenarios

◮ What’s missing is a general framework to model diverse,

decentralized actors across sectors

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The problem

◮ What’s missing is a general framework to model diverse,

decentralized actors across sectors Such a framework could answer questions like:

◮ What types of competition are likely in space industries? ◮ How will technical or economic decisions impact inter-industry

linkages?

◮ How might regulations interact with industry profitability and

Gross Space Product?

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The problem

◮ What’s missing is a general framework to model diverse,

decentralized actors across sectors Such a framework would

  • 1. allow flexibility in defining player logic and importing custom

data/models as inputs

  • 2. perform consistent aggregation of choices and outcomes
  • 3. solve for market prices and reflect policy constraints
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Agent-Based Modeling

Agent-Based Models

Agent-Based Models (ABM) are computational models in which rule-based objects (“agents”) interact independent of central control.

Our Solution: the CENKI SES

Use ABM to build up individual decisions and interactions. Agents interact in the marketplace, which tracks transactions and aggregates outcomes.

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How it works

◮ Players are collections of Agents ◮ Agents may be Customers or Providers ◮ Customers issue Contracts, and Providers submit Bids ◮ When a Customer’s best interest is fulfilled by a particular Bid

and a Provider’s best interest is fulfilled by a particular Contract, the two agents complete a Deal

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SES Overview

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Study layout

This study

We verify that the SES reproduces analytical solutions to economic models.

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Study layout

We study three models, progressively increasing in complexity.

  • 1. Model A1: Competition between two providers
  • 2. Model A2: Competition with production from reserves
  • 3. Model A3: Competition with production and supply chain

In all of these models, providers supply undifferentiated goods and customers select the lowest-cost providers (Bertrand competition).

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A1 and A2 layout

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Model A1

◮ Lines represent the

analytical model

◮ Circles represent

simulation values

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Model A2

In this model, satellites must be produced using available transponders and buses

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A3 layout

In this model, satellites must be produced using purchased transponders and buses

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Model A3

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Summary

Commercial space is complex. Complexity extends to

◮ supply chains and production decisions; ◮ random events and environmental hazards; ◮ regulatory policies and long-term agreements.

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Summary

Commercial space is complex. Complexity extends to

◮ supply chains and production decisions; ◮ random events and environmental hazards; ◮ regulatory policies and long-term agreements.

The SES addresses this complexity by

◮ allowing users to flexibly specify players and products, ◮ allowing users to supply custom inputs to players and simulate

realizations over inputs, and

◮ mediating and aggregating agent interactions through the

marketplace.

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SES with data? Use cases?

Demonstrating Agent-Based Modeling on Satellite Market Data

See 13.0205 in Canyon after this!

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THANK YOU! Questions / Comments? www.cenki.space