Augustana Invitational Robotics Challenge 2018

We will be hosting the 3rd Annual Augustana Invitational Robotics Challenge. This event will involve student teams from Augustana and potentially several other schools in the region bringing forth the robots that they have designed, built, and programmed, to compete against one another. This year\u27s challenge task involves the careful relocation of soda pop cans

Swedish Immigrant Trail Game Report

During the 2017-2018 academic year, I led a collaborative interdisciplinary project to develop a historical fiction video game focused on the Swedish immigration story. With bristling debates over modern immigration in national and international politics, it seems more important than ever for us to look to history to understand the immigrant experience, both at a personal level (immigrants\u27 lives and stories), and at a broader cultural and economic level to understand the impact they have had on our nation, and that our nation has had on them

Data Insertion in Bitcoin\u27s Blockchain

This paper provides the first comprehensive survey of methods for inserting arbitrary data into Bitcoin\u27s blockchain. Historical methods of data insertion are described, along with lesser-known techniques that are optimized for efficiency. Insertion methods are compared on the basis of efficiency, cost, convenience of data reconstruction, permanence, and potentially negative impact on the Bitcoin ecosystem

Alternate Social Theory Discovery Using Genetic Programming: Towards Better Understanding The Artificial Anasazi

A pressing issue with agent-based model (ABM) replicability is the ambiguity behind micro-behavior rules of the agents. In practice, modelers choose between competing theories, each describing separate candidate solutions. Pattern-oriented modeling (POM) and stylized facts matching recommend testing theories against patterns extracted from real-world data. Yet, manually, POM is tedious and prone to human error. In this study, we present a genetic programming strategy to evolve debatable assumptions on agent micro-behaviors. After proper modularization of the candidate micro-behaviors, genetic programming can discover candidate micro-behaviors which reproduce patterns found in real-world data. We illustrate this strategy by evolving the decision tree representing the farm-seeking strategy of agents in the Artificial Anasazi ABM. Through evolutionary theory discovery, we obtain multiple candidate decision trees for farm-seeking which fit the archaeological data better than the calibrated original model in the literature. We emphasize the necessity to explore a range of components that influence the agents\u27 decision making process and demonstrate that this is achievable through an evolutionary process if the rules are modularized as required. The end result is a set of plausible candidate solutions that closely fit the real-world data, which can then be nominated by domain experts

Evolving viral marketing strategies

One method of viral marketing involves seeding certain consumers within a population to encourage faster adoption of the product throughout the entire population. However, determining how many and which consumers within a particular social network should be seeded to maximize adoption is challenging. We define a strategy space for consumer seeding by weighting a combination of network characteristics such as average path length, clustering coefficient, and degree. We measure strategy effectiveness by simulating adoption on a Bass-like agent-based model, with five different social network structures: four classic theoretical models (random, lattice, small-world, and preferential attachment) and one empirical (extracted from Twitter friendship data). To discover good seeding strategies, we have developed a new tool, called BehaviorSearch, which uses genetic algorithms to search through the parameter-space of agent-based models. This evolutionary search also provides insight into the interaction between strategies and network structure. Our results show that one simple strategy (ranking by node degree) is near-optimal for the four theoretical networks, but that a more nuanced strategy performs significantly better on the empirical Twitter-based network. We also find a correlation between the optimal seeding budget for a network, and the inequality of the degree distribution