🤖Modeling & Simulating Bonding Curves
The importance of systems engineering and data science in token design validation
Modeling and simulating bonding curves enables the exploration of the design space. Use cases, and environmental scenarios can be considered and visualized to communicate the economic purpose of bonding curves. Configuration parameters can be explored to assist in the implementation and deployment of bonding curve economies. Modeling enables systems engineering and data science practices to be applied to the design and deployment of bonding curve ecosystems. The goals of the system can be mapped to mechanism design and parameterization to produce economic systems that benefit the communities that steward them.
Through this work, communication tools are generated that enable builders to understand the behavior, features, parameters, and risks of bonding curves. Through this work we aim to simplify the communication of bonding curves, and provide a toolkit for exploring the design space of bonding curves, and producing re-usable tooling as infrastructure for future research. To construct bonding curve models, we create parameterized classes using the param library. Param is a powerful paradigm for python programming that enables safety guarantees to be built into code. It is typing, documentation, and GUI development all baked into one convenient api that requires describing the parameters that define the data structures of a code-base. With this we can create powerful re-usable models using clean object oriented class based structures. To construct GUI interfaces that can be viewed directly in jupyter or deployed as web applications we use the complimentary panel library, which itself is built using param.
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