Programs and Communities

Since 2018, MMM team members have written sport-style summaries following the live "play-by-play" narration of simulated combatant encounters on social media. These summaries, highlighting key adaptations, human impacts, and other scholarly information, have been provided to educators to describe tournament outcomes to their learners who do not follow the battle narrations "live" on social media.

March Mammal Madness is a science outreach project that, over the course of several weeks in March, reaches hundreds of thousands of people in the United States every year. We combine four approaches to science outreach – gamification, social media platforms, community event(s), and creative products – to run a simulated tournament in which 64 animals compete to become the tournament champion. While the encounters between the animals are hypothetical, the outcomes rely on empirical evidence from the scientific literature. Players select their favored combatants beforehand, and during the tournament scientists translate the academic literature into gripping “play-by-play” narration on social media. To date ~1100 scholarly works, covering almost 400 taxa, have been transformed into science stories. March Mammal Madness is most typically used by high-school educators teaching life sciences, and we estimate that our materials reached ~1% of high-school students in the United States in 2019. Here we document the intentional design, public engagement, and magnitude of reach of the project. We further explain how human psychological and cognitive adaptations for shared experiences, social learning, narrative, and imagery contribute to the widespread use of March Mammal Madness.

This packet includes:

 2021 Bracket Common Name 

2021 Bracket Latin Binomial 

Bracket FAQ (English) 

Pre-Tournament Research Lesson Plan (English) 

Tournament Lesson Plan & Worksheets (English) 

Visual Arts Lesson Plan (English) 

Language Arts Lesson Plan (English) 

Guide for Youngest Players (English)

JUMBO Bracket for Youngest Players (English)

2021 Bracket Common Name (Spanish) 

Pre-Tournament Research Lesson Plan (Spanish) 

Tournament Lesson Plan & Worksheets (Spanish) 

Visual Arts Lesson Plan (Spanish)

Language Arts Lesson Plan (Spanish) 

JUMBO Bracket for Youngest Players (Spanish) 

Hybrid system models - those devised from two or more disparate sub-system models - provide a number of benefits in terms of conceptualization, development, and assessment of dynamical systems. The decomposition approach helps to formulate complex interactions that are otherwise difficult or impractical to express. However, hybrid model development and usage can introduce complexity that emerges from the composition itself.

To improve assurance of model correctness, sub-systems using disparate modeling formalisms must be integrated above and beyond just the data and control level; their composition must have model specification and simulation execution aspects as well. Poly-formalism composition is one approach to composing models in this manner.

This dissertation describes a poly-formalism composition between a Discrete EVent System specification (DEVS) model and a Cellular Automata (CA) model types. These model specifications have been chosen for their broad applicability in important and emerging domains. An agent-environment domain exemplifies the composition approach. The inherent spatial relations within a CA make it well-suited for environmental representations. Similarly, the component-based nature of agents fits well within the hierarchical component structure of DEVS.

This composition employs the use of a third model, called an interaction model, that includes methods for integrating the two model types at a formalism level, at a systems architecture level, and at a model execution level. A prototype framework using DEVS for the agent model and GRASS for the environment has been developed and is described. Furthermore, this dissertation explains how the concepts of this composition approach are being applied to a real-world research project.

This dissertation expands the tool set modelers in computer science and other disciplines have in order to build hybrid system models, and provides an interaction model for an on-going research project. The concepts and models presented in this dissertation demonstrate the feasibility of composition between discrete-event agents and discrete-time cellular automata. Furthermore, it provides concepts and models that may be applied directly, or used by a modeler to devise compositions for other research efforts.