Bryan Wilder

Inferring individual-level processes from population-level patterns in cultural evolution

Our species is characterized by a great degree of cultural variation, both within and between populations. Understanding how group-level patterns of culture emerge from individual-level behaviour is a long-standing question in the biological and social sciences. We develop a simulation model capturing demographic and cultural dynamics relevant to human cultural evolution, focusing on the interface between population-level patterns and individual-level processes. The model tracks the distribution of variants of cultural traits across individuals in a population over time, conditioned on different pathways for the transmission of information between individuals. From these data, we obtain theoretical expectations for a range of statistics commonly used to capture population-level characteristics (e.g. the degree of cultural diversity). Consistent with previous theoretical work, our results show that the patterns observed at the level of groups are rooted in the interplay between the transmission pathways and the age structure of the population. We also explore whether, and under what conditions, the different pathways can be distinguished based on their group-level signatures, in an effort to establish theoretical limits to inference. Our results show that the temporal dynamic of cultural change over time retains a stronger signature than the cultural composition of the population at a specific point in time. Overall, the results suggest a shift in focus from identifying the one individual-level process that likely produced the observed data to excluding those that likely did not. We conclude by discussing the implications for empirical studies of human cultural evolution.

Reconciling explanations for the evolution of evolvability

Evolution’s ability to find innovative phenotypes is an important ingredient in the emergence of complexity in nature. A key factor in this capability is evolvability, or the propensity towards phenotypic variation. Numerous explanations for the origins of evolvability have been proposed, often differing in the role that they attribute to adaptive processes. To provide a new perspective on these explanations, experiments in this paper simulate evolution in gene regulatory networks, revealing that the type of evolvability in question significantly impacts the dynamics that follow. In particular, while adaptive processes result in evolvable individuals, processes that are either neutral or that explicitly encourage divergence result in evolvable populations. Furthermore, evolvability at the population level proves the most critical factor in the production of evolutionary innovations, suggesting that nonadaptive mechanisms are the most promising avenue for investigating and understanding evolvability. These results reconcile a large body of work across biology and inform attempts to reproduce evolvability in artificial settings.

Divide to Defend: Collusive Security Games

Research on security games has focused on settings where the defender must protect against either a single adversary or multiple, independent adversaries. However, there are a variety of real-world security domains where adversaries may benefit from colluding in their actions against the defender, e.g., wildlife poaching, urban crime and drug trafficking. Given such adversary collusion may be more detrimental for the defender, she has an incentive to break up collusion by playing off the self-interest of individual adversaries. As we show in this paper, breaking up such collusion is difficult given bounded rationality of human adversaries; we therefore investigate algorithms for the defender assuming both rational and boundedly rational adversaries. The contributions of this paper include i collusive security games COSGs, a model for security games involving potential collusion among adversaries, ii SPECTRE-R, an algorithm to solve COSGs and break collusion assuming rational adversaries, iii observations and analyses of adversary behavior and the underlying factors including bounded rationality, imbalanced- resource-allocation effect, coverage perception, and individualism/collectivism attitudes within COSGs with data from 700 human subjects, iv a learned human behavioral model that incorporates these factors to predict when collusion will occur, v SPECTRE-BR, an enhanced algorithm which optimizes against the learned behavior model to provide demonstrably better performing defender strategies against human subjects compared to SPECTRE-R.

Altruists Proliferate Even at a Selective Disadvantage within Their Own Niche.

The evolutionary origin of altruism is a long-standing puzzle. Numerous explanations have been proposed, most prominently based on inclusive fitness or group selection. One possibility that has not yet been considered is that new niches will be created disproportionately often when altruism appears, perhaps by chance, causing altruists to be over-represented in such new niches. This effect is a novel variant of group selection in which altruistic groups benefit by discovering unoccupied niches instead of by competing for the limited resources within a single niche. Both an analytical population genetics model and computational simulations support that altruism systematically arises due to this side effect of increased carrying capacity even when it is strongly selected against within any given niche. In fact, even when selection is very strongly negative and altruism does not develop in most populations, it can still be expected to be observed in a consistent fraction of species. The ecological structure provided by niches thereby may be sufficient for altruists to proliferate even if they are always at a disadvantage within each niche considered individually.

Inference of Cultural Transmission Modes Based on Incomplete Information

abstract In this article we explore the theoretical limits of the inference of cultural transmission modes based on sparse population-level data. We approach this problem by investigating whether different transmission modes produce different temporal dynamics of cultural change. In particular, we explore whether different transmission modes result in sufficiently different distributions of the average time a variant stays the most common variant in the population, tmax, so that their inference can be guaranteed on the basis of an estimate of tmax. We assume time series data detailing the frequencies of different variants of a cultural trait in a population at different points in time and investigate the temporal resolution (i.e., the length of the time series and the distance between consecutive time points) that is needed to ensure distinguishability between transmission modes. We find that under complete information most transmission modes can be distinguished on the basis of the statistic tmax; however, we should not expect the same results if only infrequent information about the most common cultural variant in the population is available.

Designing Fair, Efficient, and Interpretable Policies for Prioritizing Homeless Youth for Housing Resources

We consider the problem of designing fair, efficient, and interpretable policies for prioritizing heterogeneous homeless youth on a waiting list for scarce housing resources of different types. We focus on point-based policies that use features of the housing resources (e.g., permanent supportive housing, rapid rehousing) and the youth (e.g., age, history of substance use) to maximize the probability that the youth will have a safe and stable exit from the housing program. The policies can be used to prioritize waitlisted youth each time a housing resource is procured. Our framework provides the policy-maker the flexibility to select both their desired structure for the policy and their desired fairness requirements. Our approach can thus explicitly trade-off interpretability and efficiency while ensuring that fairness constraints are met. We propose a flexible data-driven mixed-integer optimization formulation for designing the policy, along with an approximate formulation which can be solved efficiently for broad classes of interpretable policies using Bender’s decomposition. We evaluate our framework using real-world data from the United States homeless youth housing system. We show that our framework results in policies that are more fair than the current policy in place and than classical interpretable machine learning approaches while achieving a similar (or higher) level of overall efficiency.

AI in Informal Science Education: Bringing Turing Back to Life to Perform the Turing Test

This paper describes an interactive museum exhibit featuring an avatar of Alan Turing that informs museum visitors about artificial intelligence and Turing’s seminal Turing Test for machine intelligence. The objective of the exhibit is to engage and motivate visiting children in the hope of sparking an interest in them about computer science and artificial intelligence, and cause them to consider pursuing future studies and/or careers in these fields. The exhibit interacts with the visitors, allowing them to participate in a simplified version of Turing’s test that is brief and informal to suit the limitations of a five-minute exhibit. In this exhibit, the visitor (targeted towards middle school age children) invokes an avatar of his/her own choice, and acts to endow it with human-like qualities (voice, brain, eyesight and hearing). Then, the visitor engages the avatar in a (brief) question-and-answer session to determine whether the visitor thinks that he/she is interacting with a real human on a video conference or with an avatar. We consider this interaction to be an extension of the original Turing Test because, unlike Turing’s original that used text via a teletype, this version features a graphical embodiment of an agent with which one can converse in spoken natural language. This extension serves to make passing the Turing Test more difficult, as now the avatar must not only communicate like a human, but also look, sound and act the part. It also makes the exhibit visual, dynamic and interesting to the visitors. Evaluations were performed with museum visitors, both in backrooms with prototypes as well as on the museum floor with the final version. The formative and summative evaluations performed indicated overall success in engaging the museum visitors and increasing their interest in computer science. More specifically, the formative testing, mostly done in quiet back rooms with selected test subjects, indicated that on the important questions about enjoyment of exhibit and increased interest in computer science by the test subjects, their self-reported Likert scale responses (1 being negative and 5 being positive) increased from 3.16 in the first evaluation to 4.38 in the third one for increased interest in CS. Likewise for the question about exhibit enjoyment (from 3.92 to 4.56). The summative evaluation, done through unobtrusive observation of exhibit use on museum floor, indicated that almost 74% of the parties that initiated the exhibit were either highly or moderately engaged by the exhibit. However, there was one major negative finding, namely the overly long duration of the exhibit, which may have caused premature abandonment of the exhibit in several cases during the summative evaluation. These tests and their results are presented and discussed in detail in this paper. The exhibit has been on permanent display at the Orlando (FL) Science Center since June 2014 and has received a strongly positive response from visitors since that time.

Bridging the Gap Between Theory and Practice in Influence Maximization: Raising Awareness about HIV among Homeless Youth.

This paper reports on results obtained by deploying HEALER and DOSIM (two AI agents for social influence maximization) in the real-world, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key ”seed” nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, the usability of these AI agents in the real-world was unknown. This paper presents results from three real-world pilot studies involving 173 homeless youth across two different homeless shelters in Los Angeles. The results from these pilot studies illustrate that HEALER and DOSIM outperform the current modus operandi of service providers by ∼160% in terms of information spread about HIV among homeless youth.

Remembering a Conversation – A Conversational Memory Architecture for Embodied Conversational Agents

Abstract This paper addresses the role of conversational memory in Embodied Conversational Agents (ECAs). It describes an investigation into developing such a memory architecture and integrating it into an ECA. ECAs are virtual agents whose purpose is to engage in conversations with human users, typically through natural language speech. While several works in the literature seek to produce viable ECA dialog architectures, only a few authors have addressed the episodic memory architectures in conversational agents and their role in enhancing their intelligence. In this work, we propose, implement, and test a unified episodic memory architecture for ECAs. We describe a process that determines the prevalent contexts in the conversations obtained from the interactions. The process presented demonstrates the use of multiple techniques to extract and store relevant snippets from long conversations, most of whose contents are unremarkable and need not be remembered. The mechanisms used to store, retrieve, and recall episodes from previous conversations are presented and discussed. Finally, we test our episodic memory architecture to assess its effectiveness. The results indicate moderate success in some aspects of the memory-enhanced ECAs, as well as some work still to be done in other aspects.

On the Critical Role of Divergent Selection in Evolvability

An ambitious goal in evolutionary robotics is to evolve increasingly complex robotic behaviors with minimal human design effort. Reaching this goal requires evolutionary algorithms that can unlock from genetic encodings their latent potential for evolvability. One issue clouding this goal is conceptual confusion about evolvability, which often obscures the aspects of evolvability that are important or desirable. The danger from such confusion is that it may establish unrealistic goals for evolvability that prove unproductive in practice. An important issue separate from conceptual confusion is the common misalignment between selection and evolvability in evolutionary robotics. While more expressive encodings can represent higher-level adaptations (e.g. sexual reproduction or developmental systems) that increase long-term evolutionary potential (i.e. evolvability), realizing such potential requires gradients of fitness and evolvability to align. In other words, selection is often a critical factor limiting increasing evolvability. Thus, drawing from a series of recent papers, this article seeks to both (1) clarify and focus the ways in which the term evolvability is used within artificial evolution, and (2) argue for the importance of one type of selection, i.e. divergent selection, for enabling evolvability. The main argument is that there is a fundamental connection between divergent selection and evolvability (on both the individual and population level) that does not hold for typical goal-oriented selection. The conclusion is that selection pressure plays a critical role in realizing the potential for evolvability, and that divergent selection in particular provides a principled mechanism for encouraging evolvability in artificial evolution.