For waste management in Asunción, Paraguay to improve, so too must the rate of public recycling participation. However, due to minimal public waste management infrastructure, it is up to individual citizens and the private sector to develop recycling solutions in the city. One social enterprise called Soluciones Ecológicas (SE) has deployed a system of drop-off recycling stations called ecopuntos, which allow residents to deposit their paper and cardboard, plastic, and aluminum. For SE to maximize the use of its ecopuntos, it must understand the perceived barriers to, and benefits of, their use. To identify these barriers and benefits, a doer on-doer survey based on the behavioral determinants outlined in the Designing for Behavior Change Framework was distributed among Asunción residents. Results showed that perceived self-efficacy, perceived social norms, and perceived positive consequences – as well as age – were influential in shaping ecopunto use. Other determinants such as perceived negative consequences, access, and universal motivators were significant predictors of gender and age. SE and other institutions looking to improve recycling can use these results to design effective behavior change interventions.

Optimal foraging theory provides a suite of tools that model the best way that an animal will <br/>structure its searching and processing decisions in uncertain environments. It has been <br/>successful characterizing real patterns of animal decision making, thereby providing insights<br/>into why animals behave the way they do. However, it does not speak to how animals make<br/>decisions that tend to be adaptive. Using simulation studies, prior work has shown empirically<br/>that a simple decision-making heuristic tends to produce prey-choice behaviors that, on <br/>average, match the predicted behaviors of optimal foraging theory. That heuristic chooses<br/>to spend time processing an encountered prey item if that prey item's marginal rate of<br/>caloric gain (in calories per unit of processing time) is greater than the forager's<br/>current long-term rate of accumulated caloric gain (in calories per unit of total searching<br/>and processing time). Although this heuristic may seem intuitive, a rigorous mathematical<br/>argument for why it tends to produce the theorized optimal foraging theory behavior has<br/>not been developed. In this thesis, an analytical argument is given for why this<br/>simple decision-making heuristic is expected to realize the optimal performance<br/>predicted by optimal foraging theory. This theoretical guarantee not only provides support<br/>for why such a heuristic might be favored by natural selection, but it also provides<br/>support for why such a heuristic might a reliable tool for decision-making in autonomous<br/>engineered agents moving through theatres of uncertain rewards. Ultimately, this simple<br/>decision-making heuristic may provide a recipe for reinforcement learning in small robots<br/>with little computational capabilities.

Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

Enantiomers are pairs of non-superimposable mirror-image molecules. One molecule in the pair is the clockwise version (+) while the other is the counterclockwise version (-). Some pairs have divergent odor qualities, e.g. L-carvone (“spearmint”) vs. D-carvone (“caraway”), while other pairs do not. Existing theory about the origin of such differences is largely qualitative (Friedman and Miller, 1971; Bentley, 2006; Brookes et al., 2008). While quantitative models based on intrinsic molecular features predict some structure–odor relationships (Keller et al., 2017), they cannot identify, e.g. the more intense enantiomer in a pair; the mathematical operations underlying such features are invariant under symmetry (Shadmany et al., 2018). Only the olfactory receptor (OR) can break this symmetry because each molecule within an enantiomeric pair will have a different binding configuration with a receptor. However, features that predict odor divergence within a pair may be identifiable; for example, six-membered ring flexibility has been offered as a candidate (Brookes et al., 2008). To address this problem, we collected detection threshold data for >400 molecules (organized into enantiomeric pairs) from a variety of public data sources and academic literature. From each pair, we computed the within-pair divergence in odor detection threshold, as well as Mordred descriptors (molecular features derived from the structure of a molecule) and Morgan fingerprints (mathematical representations of molecule structure). While these molecular features are identical within-pair (due to symmetry), they remain distinct across pairs. The resulting structure+perception dataset was used to build a predictive model of odor detection threshold divergence. It predicted a modest fraction of variance in odor detection threshold divergence (r 2 ~ 0.3 in cross-validation). We speculate that most of the remaining variance could be explained by a better understanding of the ligand-receptor binding process.