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.

Much like neighboring nations, living in close proximity can often lead to conflict over limited resources for social insect colonies. As with warring nations, conflicts among insect societies can also result in one colony attempting to invade the other. Though emigrations are common and well understood in social insects, the process of emigration in the context of conflict is not known. During emigrations of the ant Temnothorax rugatulus, colonies first employ the use of scouts, who search for new nest locations. These scouts then recruit naïve workers to these nests resulting in a ‘voting’ process through which colonies can collectively choose the best nest site. Once the decision is made, the selected nest is rapidly populated by workers who physically carry the queen(s), brood, and remaining naïve ants to the new nest. Invasions occurring during inter-colony conflicts bear a striking resemblance to this process. The state of the final nest suggested merged colonies, and statistical models were used to test for the likelihood of this. Here we test whether colonies of T. rugatulus use the same mechanisms during invasions as those used in emigrations by observing conflicts between colonies of T. rugatulus ants and tracking instances of scouting and recruitment, transport and changes in populations in each nest. Our results support the predicted order of behaviors starting with scouting, followed by recruitment and transport last. In addition, presence of the quorum rule, which determines the switch from recruitment to transport, is confirmed. Furthermore, evidence showed that the colonies were merged at the time of transport. While ant emigration patterns are well understood, there is a gap in understanding conflict driven emigrations/invasions. Our results serve to better understand conflict in social insects by further understanding the mechanisms used during conflicts.

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.

Olfactory discrimination tasks can provide useful information about how olfaction may have evolved by demonstrating which types of compounds animals will detect and respond to. Ants discriminate between nestmates and non-nestmates by using olfaction to detect the cuticular hydrocarbons on other ants, and Camponotus floridanus have particularly clear and aggressive responses to non-nestmates. A new method of adding hydrocarbons to ants, the “Snow Globe” method was further optimized and tested on C. floridanus. It involves adding hydrocarbons and a solvent to a vial of water, vortexing it, suspending hydrocarbon droplets throughout the solution, and then dipping a narcotized ant in. It is hoped this method can evenly coat ants in hydrocarbon. Ants were treated with heptacosane (C27), nonacosane (C29), hentriacontane (C31), a mixture of C27/C29/C31, 2-methyltriacontane (2MeC30), S-3-methylhentriacontane (SMeC31), and R-3-methylhentriacontane (RMeC31). These were chosen to see how ants reacted in a nestmate recognition context to methyl-branched hydrocarbons, R and S enantiomers, and to multiple added alkanes. Behavior assays were performed on treated ants, as well as two untreated controls, a foreign ant and a nestmate ant. There were 15 replicates of each condition, using 15 different queenright colonies. The Snow Globe method successfully transfers hydrocarbons, as confirmed by solid phase microextraction (SPME) done on treated ants, and the behavior assay data shows the foreign control, SMeC31, and the mixture of C27/29/31 were all statistically significant in their differences from the native control. The multiple alkane mixture received a significant response while single alkanes did not, which supports the idea that larger variations in hydrocarbon profile are needed for an ant to be perceived as foreign. The response to SMeC31 shows C. floridanus can respond during nestmate recognition to hydrocarbons that are not naturally occurring, and it indicates the nestmate recognition process may simply be responding to any compounds not found in the colony profile and rather than detecting particular foreign compounds.

In this project, I analyze representative samples from three different fashion brands’ sustainability-related informational materials provided to the public through their websites, annual reports, and clothing tags that promote the company’s environmental initiatives. The three companies were chosen because they each represent global fashion- they are all extremely large, popular, and prevalent brands. These materials are evaluated against three frameworks for identifying deceptive greenwashing claims. I identify instances in which these frameworks are successful in categorizing deceptive claims from these companies as well as instances in which they appear to be vulnerable. To address the vulnerabilities I discover in the three existing frameworks for identifying greenwashing, I propose six new guidelines to be used in conjunction with these frameworks that will help to ensure that consumers can have a more ample toolbox to identify deceptive sustainability claims.