Behavioral and Network Origins of Wealth Inequality: Insights from a Virtual World

Almost universally, wealth is not distributed uniformly within societies or economies. Even though wealth data have been collected in various forms for centuries, the origins for the observed wealth-disparity and social inequality are not yet fully understood. Especially the impact and connections of human behavior on wealth could so far not be inferred from data. Here we study wealth data from the virtual economy of the massive multiplayer online game (MMOG) Pardus. This data not only contains every player''s wealth at every point in time, but also all actions over a timespan of almost a decade. We find that wealth distributions in the virtual world are very similar to those in Western countries. In particular we find an approximate exponential distribution for low wealth levels and a power-law tail for high levels. The Gini index is found to be , which is close to the indices of many Western countries. We find that wealth-increase rates depend on the time when players entered the game. Players that entered the game early on tend to have remarkably higher wealth-increase rates than those who joined later. Studying the players'' positions within their social networks, we find that the local position in the trade network is most relevant for wealth. Wealthy people have high in- and out-degrees in the trade network, relatively low nearest-neighbor degrees, and low clustering coefficients. Wealthy players have many mutual friendships and are socially well respected by others, but spend more time on business than on socializing. Wealthy players have few personal enemies, but show animosity towards players that behave as public enemies. We find that players that are not organized within social groups are significantly poorer on average. We observe that “political” status and wealth go hand in hand.     

Adaptive search space pruning in complex strategic problems

People have limited computational resources, yet they make complex strategic decisions over enormous spaces of possibilities. How do people efficiently search spaces with combinatorially branching paths? Here, we study players’ search strategies for a winning move in a “k-in-a-row” game. We find that players use scoring strategies to prune the search space and augment this pruning by a “shutter” heuristic that focuses the search on the paths emanating from their previous move. This strong pruning has its costs—both computational simulations and behavioral data indicate that the shutter size is correlated with players’ blindness to their opponent’s winning moves. However, simulations of the search while varying the shutter size, complexity levels, noise levels, branching factor, and computational limitations indicate that despite its costs, a narrow shutter strategy is the dominant strategy for most of the parameter space. Finally, we show that in the presence of computational limitations, the shutter heuristic enhances the performance of deep learning networks in these end-game scenarios. Together, our findings suggest a novel adaptive heuristic that benefits search in a vast space of possibilities of a strategic game.     

Evolutionary Stability in the Asymmetric Volunteer's Dilemma

It is often assumed that in public goods games, contributors are either strong or weak players and each individual has an equal probability of exhibiting cooperation. It is difficult to explain why the public good is produced by strong individuals in some cooperation systems, and by weak individuals in others. Viewing the asymmetric volunteer''s dilemma game as an evolutionary game, we find that whether the strong or the weak players produce the public good depends on the initial condition (i.e., phenotype or initial strategy of individuals). These different evolutionarily stable strategies (ESS) associated with different initial conditions, can be interpreted as the production modes of public goods of different cooperation systems. A further analysis revealed that the strong player adopts a pure strategy but mixed strategies for the weak players to produce the public good, and that the probability of volunteering by weak players decreases with increasing group size or decreasing cost-benefit ratio. Our model shows that the defection probability of a “strong” player is greater than the “weak” players in the model of Diekmann (1993). This contradicts Selten''s (1980) model that public goods can only be produced by a strong player, is not an evolutionarily stable strategy, and will therefore disappear over evolutionary time. Our public good model with ESS has thus extended previous interpretations that the public good can only be produced by strong players in an asymmetric game.     

Leading the Game,Losing the Competition: Identifying Leaders and Followers in a Repeated Game

We explore a new method for identifying leaders and followers, LF, in repeated games by analyzing an experimental, repeated (50 rounds) game where Row player shifts the payoff between small and large values–a type of “investor” and Column player determines who gets the payoff–a type of “manager”. We found that i) the Investor (Row) most often is a leading player and the manager (Column) a follower. The longer the Investor leads the game, the higher is both player’s payoff. Surprisingly however, it is always the Manager that achieves the largest payoff. ii) The game has an efficient cooperative strategy where the players alternate in receiving a high payoff, but the players never identify, or accept, that strategy. iii) Under the assumption that the information used by the players is closely associated with the leader- follower sequence, and that information is available before the player’s decisions are made, the players switched LF- strategy primarily as a function of information on the Investor’s investment and moves and secondly as a function of the Manager’s payoff.     

Reciprocity on the Hardwood: Passing Patterns among Professional Basketball Players

Past theory and research view reciprocal resource sharing as a fundamental building block of human societies. Most studies of reciprocity dynamics have focused on trading among individuals in laboratory settings. But if motivations to engage in these patterns of resource sharing are powerful, then we should observe forms of reciprocity even in highly structured group environments in which reciprocity does not clearly serve individual or group interests. To this end, we investigated whether patterns of reciprocity might emerge among teammates in professional basketball games. Using data from logs of National Basketball Association (NBA) games of the 2008–9 season, we estimated a series of conditional logistic regression models to test the impact of different factors on the probability that a given player would assist another player in scoring a basket. Our analysis found evidence for a direct reciprocity effect in which players who had “received” assists in the past tended to subsequently reciprocate their benefactors. Further, this tendency was time-dependent, with the probability of repayment highest soon after receiving an assist and declining as game time passed. We found no evidence for generalized reciprocity – a tendency to “pay forward” assists – and only very limited evidence for indirect reciprocity – a tendency to reward players who had sent others many assists. These findings highlight the power of reciprocity to shape human behavior, even in a setting characterized by extensive planning, division of labor, quick decision-making, and a focus on inter-group competition.     

Agency Modulates the Lateral and Medial Prefrontal Cortex Responses in Belief-Based Decision Making

Many real-life decisions in complex and changing environments are guided by the decision maker’s beliefs, such as her perceived control over decision outcomes (i.e., agency), leading to phenomena like the “illusion of control”. However, the neural mechanisms underlying the “agency” effect on belief-based decisions are not well understood. Using functional imaging and a card guessing game, we revealed that the agency manipulation (i.e., either asking the subjects (SG) or the computer (CG) to guess the location of the winning card) not only affected the size of subjects’ bets, but also their “world model” regarding the outcome dependency. Functional imaging results revealed that the decision-related activation in the lateral and medial prefrontal cortex (PFC) was significantly modulated by agency and previous outcome. Specifically, these PFC regions showed stronger activation when subjects made decisions after losses than after wins under the CG condition, but the pattern was reversed under the SG condition. Furthermore, subjects with high external attribution of negative events were more affected by agency at the behavioral and neural levels. These results suggest that the prefrontal decision-making system can be modulated by abstract beliefs, and are thus vulnerable to factors such as false agency and attribution.     

The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions

In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our “worst-case weighted multi-objective game” model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call “robust-weighted Nash equilibrium”. We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.     

Evolution of Conformity in Social Dilemmas

People often deviate from their individual Nash equilibrium strategy in game experiments based on the prisoner’s dilemma (PD) game and the public goods game (PGG), whereas conditional cooperation, or conformity, is supported by the data from these experiments. In a complicated environment with no obvious “dominant” strategy, conformists who choose the average strategy of the other players in their group could be able to avoid risk by guaranteeing their income will be close to the group average. In this paper, we study the repeated PD game and the repeated m-person PGG, where individuals’ strategies are restricted to the set of conforming strategies. We define a conforming strategy by two parameters, initial action in the game and the influence of the other players’ choices in the previous round. We are particularly interested in the tit-for-tat (TFT) strategy, which is the well-known conforming strategy in theoretical and empirical studies. In both the PD game and the PGG, TFT can prevent the invasion of non-cooperative strategy if the expected number of rounds exceeds a critical value. The stability analysis of adaptive dynamics shows that conformity in general promotes the evolution of cooperation, and that a regime of cooperation can be established in an AllD population through TFT-like strategies. These results provide insight into the emergence of cooperation in social dilemma games.     

Spontaneous decisions and operant conditioning in fruit flies

Already in the 1930s Skinner, Konorskiand colleagues debated the commonalities, differences and interactions among the processes underlying what was then known as “conditioned reflexes type I and II”, but which is today more well-known as classical (Pavlovian) and operant (instrumental) conditioning. Subsequent decades of research have confirmed that the interactions between the various learning systems engaged during operant conditioning are complex and difficult to disentangle. Today, modern neurobiological tools allow us to dissect the biological processes underlying operant conditioning and study their interactions. These processes include initiating spontaneous behavioral variability, world-learning and self-learning. The data suggest that behavioral variability is generated actively by the brain, rather than as a by-product of a complex, noisy input-output system. The function of this variability, in part, is to detect how the environment responds to such actions. World-learning denotes the biological process by which value is assigned to environmental stimuli. Self-learning is the biological process which assigns value to a specific action or movement. In an operant learning situation using visual stimuli for flies, world-learning inhibits self-learning via a prominent neuropil region, the mushroom-bodies. Only extended training can overcome this inhibition and lead to habit formation by engaging the self-learning mechanism. Self-learning transforms spontaneous, flexible actions into stereotyped, habitual responses.     

Mushroom Bodies Regulate Habit Formation in Drosophila

To make good decisions, we evaluate past choices to guide later decisions. In most situations, we have the opportunity to simultaneously learn about both the consequences of our choice (i.e., operantly) and the stimuli associated with correct or incorrect choices (i.e., classically) [1]. Interestingly, in many species, including humans, these learning processes occasionally lead to irrational decisions [2]. An extreme case is the habitual drug user consistently administering the drug despite the negative consequences, but we all have experience with our own, less severe habits. The standard animal model employs a combination of operant and classical learning components to bring about habit formation in rodents [3] and [4]. After extended training, these animals will press a lever even if the outcome associated with lever-pressing is no longer desired [5]. In this study, experiments with wild-type and transgenic flies revealed that a prominent insect neuropil, the mushroom bodies (MBs), regulates habit formation in flies by inhibiting the operant learning system when a predictive stimulus is present. This inhibition enables generalization of the classical memory and prevents premature habit formation. Extended training in wild-type flies produced a phenocopy of MB-impaired flies, such that generalization was abolished and goal-directed actions were transformed into habitual responses.     

Inferring on the Intentions of Others by Hierarchical Bayesian Learning

Inferring on others'' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to “player” or “adviser” roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart. The adviser, who received more predictive information, issued an additional recommendation. Critically, the game was structured such that the adviser''s incentives to provide helpful or misleading information varied in time. Using a meta-Bayesian modeling framework, we found that the players'' behavior was best explained by the deployment of hierarchical learning: they inferred upon the volatility of the advisers'' intentions in order to optimize their predictions about the validity of their advice. Beyond learning, volatility estimates also affected the trial-by-trial variability of decisions: participants were more likely to rely on their estimates of advice accuracy for making choices when they believed that the adviser''s intentions were presently stable. Finally, our model of the players'' inference predicted the players'' interpersonal reactivity index (IRI) scores, explicit ratings of the advisers'' helpfulness and the advisers'' self-reports on their chosen strategy. Overall, our results suggest that humans (i) employ hierarchical generative models to infer on the changing intentions of others, (ii) use volatility estimates to inform decision-making in social interactions, and (iii) integrate estimates of advice accuracy with non-social sources of information. The Bayesian framework presented here can quantify individual differences in these mechanisms from simple behavioral readouts and may prove useful in future clinical studies of maladaptive social cognition.     

The dopamine circuit as a reward-taxis navigation system

Studying the brain circuits that control behavior is challenging, since in addition to their structural complexity there are continuous feedback interactions between actions and sensed inputs from the environment. It is therefore important to identify mathematical principles that can be used to develop testable hypotheses. In this study, we use ideas and concepts from systems biology to study the dopamine system, which controls learning, motivation, and movement. Using data from neuronal recordings in behavioral experiments, we developed a mathematical model for dopamine responses and the effect of dopamine on movement. We show that the dopamine system shares core functional analogies with bacterial chemotaxis. Just as chemotaxis robustly climbs chemical attractant gradients, the dopamine circuit performs ‘reward-taxis’ where the attractant is the expected value of reward. The reward-taxis mechanism provides a simple explanation for scale-invariant dopaminergic responses and for matching in free operant settings, and makes testable quantitative predictions. We propose that reward-taxis is a simple and robust navigation strategy that complements other, more goal-directed navigation mechanisms.     

“Hand down,Man down.” Analysis of Defensive Adjustments in Response to the Hot Hand in Basketball Using Novel Defense Metrics

The hot-hand phenomenon, according to which a player’s performance is significantly elevated during certain phases relative to the expected performance based on the player’s base rate, has left many researchers and fans in basketball puzzled: The vast majority of players, coaches and fans believe in its existence but statistical evidence supporting this belief has been scarce. It has frequently been argued that the hot hand in basketball is unobservable because of strategic adjustments and defensive interference of the opposing team. We use a dataset with novel metrics, such as the number of defenders and the defensive intensity for each shot attempt, which enable us to directly measure defensive pressure. First, we examine how the shooting percentage of NBA players changes relative to the attributes of each metric. We find that it is of lesser importance by how many defenders a player is guarded but that defensive intensity, e.g., whether a defender raises his hand when his opponent shoots, has a larger impact on shot difficulty. Second, we explore how the underlying metrics and shooting accuracy change as a function of streak length. Our results indicate that defensive pressure and shot difficulty increase (decrease) during hot (cold) streaks, so that defenders seem to behave according to the hot-hand belief and try to force hot players into more difficult shots. However, we find that shooting percentages of presumably hot players do not increase and that shooting performance is not related to streakiness, so that the defenders’ hot-hand behavior cannot be considered ecologically rational. Therefore, we are unable to find evidence in favor of the hot-hand effect even when accounting for defensive pressure.     

A Biotic Game Design Project for Integrated Life Science and Engineering Education

Engaging, hands-on design experiences are key for formal and informal Science, Technology, Engineering, and Mathematics (STEM) education. Robotic and video game design challenges have been particularly effective in stimulating student interest, but equivalent experiences for the life sciences are not as developed. Here we present the concept of a "biotic game design project" to motivate student learning at the interface of life sciences and device engineering (as part of a cornerstone bioengineering devices course). We provide all course material and also present efforts in adapting the project''s complexity to serve other time frames, age groups, learning focuses, and budgets. Students self-reported that they found the biotic game project fun and motivating, resulting in increased effort. Hence this type of design project could generate excitement and educational impact similar to robotics and video games.

This Education article is part of the Education Series.

Hands-on robotic and video game design projects and competitions are widespread and have proven particularly effective at sparking interest and teaching K–12 and college students in mechatronics, computer science, and Science, Technology, Engineering, and Mathematics (STEM). Furthermore, these projects foster teamwork, self-learning, design, and presentation skills [1,2]. Such playful and interactive media that provide fun, creative, open-ended learning experiences for all ages are arguably underdeveloped in the life sciences. Most hands-on education occurs in traditionally structured laboratory courses with a few exceptions like the International Genetically Engineered Machine (iGEM) competition [3]. Furthermore, there is an increasing need to bring the traditional engineering and life science disciplines together. In order to fill these gaps, we present the concept of a biotic game design project to foster student development in a broad set of engineering and life science skills in an integrated manner (Fig. 1). Though we primarily discuss our specific implementation as a cornerstone project-based class [4], alternative implementations are possible to motivate a variety of learning goals under various constraints such as student age and cost (see supplements for all course material).Open in a separate windowFig 1We developed a bioengineering devices course that employed biotic game design as a motivating project scheme. A: Biotic games enable human players to interact with cells. B: Conceptual overview of a biotic game setup. C: Students built and played biotic games. Image credits: A C64 joystick by Speed-link, 1984 (http://commons.wikimedia.org/wiki/File:Joystick_black_red_petri_01.svg); Euglena viridis by C. G. Ehrenberg, 1838; C Photo, N. J. C.Biotic games are games that operate on biological processes (Fig. 1) [5]. The biotic games we present here involve the single-celled phototactic eukaryote, Euglena gracilis. These microscopic organisms are housed in a microfluidic chip and are displayed in a magnified image on a video screen. Players interact with these cells by modulating the intensity and direction of light perpendicular to the microfluidic chip via a joystick, thereby influencing the cells’ phototactic motion. Software tracks the position of individual euglena with respect to virtual objects overlaid on the screen, creating myriad opportunities for creative game design and play. For example, in a simple game, points might be scored when a cell hits a virtual box (see S1 Video).The biotic game design project we developed was intended to motivate all the broad categories of theoretical and hands-on skills for creating any integrated instrument intended to house and to interface with biological materials, i.e., optics, electronics, sensing, actuation, microfluidics, fabrication, image processing, programming, and creative design. We termed the synthesis of these skills “biotics” in analogy to mechatronics. Our intended audience for this course was bioengineering undergraduate students at Stanford University who already had some programming experience but little to no experience in device design, fabrication, and integration. We also incorporated bioethics into the curriculum to emphasize the social responsibility of every engineer and demonstrate the potential for the biotic game project to motivate multiple fields. The course we taught spanned ten weeks, divided roughly equally into a set of technical units and the biotic game project, with two 4-hour lab sections and a single 1.5-hour lecture each week. For details and all course documents, please refer to the supplemental material.The technical section of the course focused on developing hands-on skills and theoretical understanding related to devices in a conventionally structured laboratory setting. We introduced students to fundamental electronics concepts and components such as voltage, current, resistors, capacitors, LEDs, filters, operational amplifiers, motors, microcontrollers (Arduino Uno), and breadboards. We followed a similar traditional approach in introducing optics, presenting the thin lens equation, ray tracing, conjugate planes, basic optical system design, and Köhler illumination. We covered additional topics in less detail: MATLAB programming, particle tracking, computer-aided design (CAD), fabrication, and microfluidics (learning objectives are provided at the beginning of each unit in the supplemental material).During the project-based section, students built their own biotic games. We left specific choices of implementation, architecture, and design to the students to encourage creativity and exploration but required students to revisit the technical skills they learned in the first section by integrating some specific requirements into their games (Fig. 2). Students built a bright field microscope with Köhler illumination and projected their images onto a webcam (optics). Glass and polydimethylsiloxane (PDMS) components comprised the microfluidic chip (microfluidics) and housed the euglena (microbiology). The holder for the chip and euglena-steering LEDs was designed in Solidworks (CAD) and 3-D printed (fabrication). The students constructed a polycarbonate housing for the game controller using a band saw and drill press (fabrication). The students revisited electronic breadboarding and soldering when creating the electronic circuits to communicate between the LEDs, joystick, microcontroller, and computer. Finally, they used MATLAB to program the microcontroller, implement real time image recognition, and provide the user interface for the game experience (image processing and programming).Open in a separate windowFig 2Biotic game-based courses encourage students to integrate a versatile set of relevant STEM topics.Image credits: Taken by N. J. C. (credit for the work and artifacts to the students who took the course).We challenged students to consider the ethical implications [6] of manipulating life in a game context before building their projects. Although phototaxis experiments with euglena are commonplace in education, and have hitherto raised no ethical concerns, the equivalent manipulation in the form of a game warrants its own ethical analysis as provided by Harvey et al. [7]. The students read and discussed this paper, then wrote a 200-word essay on whether they found it permissible or not to make and play biotic games. Students had the choice to switch to a nongame project of equivalent complexity. All students found euglena-based games permissible, pointing out that “they are nonsentient and cannot feel pain,” followed by a diverse range of considerations such as “the euglena are still free to act as they please,” “there needs to be an educational intention,” or “a pet…provides a way…to work on responsibility and caring.” Based on further student-initiated discussions that spontaneously emerged throughout the course, we believe that biotic games are effective in providing a stimulating, student-relevant, in-class context for bioethics.We motivated the game design project to the students as having educational potential at two levels, i.e., learning by building and learning by playing; we lectured them about the needs and opportunities for new approaches to K–12 STEM education [8,9]. The students were then asked to consider building a game that had educational value for the player. Educational value has many aspects, which was reflected in students’ statements regarding their intended educational outcomes for their games on their course project websites. These ranged from more factual learning objectives (“learn about…” “…inner working,” “…structural detail,” “… light responses,” “…euglena behavior”) to objectives affecting attitude (“spark interest,” “generate fascination,” “encourage to explore,” “respect for life”). We also had a game designer give a guest lecture to the students. For pragmatic reasons, we requested the students keep games very simple (ideally having just a single in-game objective) and cap game duration at one minute. Before, during, and after their projects, students received feedback from instructors as well as from their peers on their games from technical and user perspectives.The games that the students ultimately produced were diverse and creative (Fig. 2 and S1 Video), including single and multiplayer scenarios, games where euglena hit virtual targets, and games where euglena pushed virtual objects. Games that involved pushing objects across the screen (relying on collective motion of many organisms) were generally more consistent at correlating player strategy to scored points than those that involved hitting target objects. The quality and robustness of these integrated projects naturally varied, and individual groups placed more or less emphasis on different aspects based on personal preferences and learning goals (for example, fabricating a more elaborate housing for the game controller versus programming more complex game mechanics). A key point was that the students did not rely on prepared materials or platforms to develop their games but rather had to design, build, and test their game setups from scratch, thereby revisiting and deepening the primary learning goals of the course with some freedom to follow their own learning aspirations (Fig. 2). The final project deliverables were a two-minute project demonstration video, a website describing the elements of the project, and a game that all instructors and students played on the final day (Fig. 1B), which led to lots of laughter as well as in-depth discussions on technical details.Many students self-reported that they enjoyed the project and that it led to increased motivation and effort during the course. In response to the question “Do you think you were motivated to try harder or had more fun (and thereby learned more) during your final project because you were making a game (rather than just building a technical instrument, for example)? If so—please give some examples:” 15 out of 17 students responded “Very/definitely” on a five point scale. As examples, students listed: “wanted to make the best game,” “want to make it clever and cool in the eyes of classmates who are play testing,” “motivated during final push,” “willing to put in more time,” “was fun”/”made it fun,” “create a game that actually works,” “reinforced what was learned before,” and “provided room for creativity.” These comments reflect the overall excitement we saw for the biotic game project. While these responses do not constitute rigorous proof regarding course effectiveness (which will require more detailed and controlled assessments in the future), we consider this course a success based on our teaching experiences.45 students have now taken this class over the past three years, with 18 students in our most recent offering. We used each year to iterate and improve our implementation. For example, we changed the organism and stimulus from Paramecia galvanotaxis [5] to Euglena phototaxis, which gave more reliable long-term responses. We also added a simple microfluidics unit enabling students to build more robust organism housing chambers. We changed the microscope structure from LEGO to Thorlabs parts (essentially trading the emphasis on 3-D structural design, flexibility, and cost for a more in-depth focus on high-end optics and their alignment). Finally, we explicitly asked the students to design and fabricate a housing for the game controller to better incorporate fabrication skills like using a band saw and tapping screw threads. So far, we primarily used MATLAB as the programming component given its widespread use in education and research and the available Arduino interface. However, MATLAB is not particularly well-suited to support game design and is also not free, making translation into lower resource settings challenging. For the future, we are considering moving to smartphone-based control (such as Android) given that these mobile environments are very flexible and increasingly used for control of scientific and consumer instruments and are becoming more widespread in education. We also see the opportunity to better emphasize and teach the approach of iterative design; for example, by letting students prototype and test their game ideas on paper [10] and simple programming environments like Scratch [11] first, before attempting the full implementation. It would likely also be very rewarding for the students to be able to take their project home at the end of the course. In summary, many different course design decisions can be made based on specific intended educational outcomes. Not all of these can be fit into one course at the same time, and clear decisions should be made on how to balance covering a breadth of topics with depth on a selected few.As a preliminary test of another age range, time frame, and budget, we taught a greatly simplified 3-hour workshop where high school and middle school students assembled a low-cost microscope and microfluidics chamber, attached it to a smartphone, and stimulated euglena using a preprogrammed Arduino-based controller (see supplements). We had no game interface implemented yet on the phone, but the students could observe the euglena responses to the light stimuli. All students were able to complete the project and take their microscopes home. Over half of our undergraduate student teams also volunteered to present their game projects for this outreach event which took place multiple weeks after their class had ended. This separate experience suggests that the biotic game concept holds promise for reaching a wider age range in a shortened timespan and at a greatly reduced budget, and that completed games can be used in outreach activities. We are currently developing a kit modeled after this unit.In conclusion, we consider biotic games promising in motivating integrated, hands-on learning at the interface of life science and engineering. Our efforts so far indicate that this concept could be adapted to various age groups and learning goals with the potential for wider future impacts on education. We draw upon the analogy to robotics, where microcontrollers went from initially unfathomable as an educational tool to the vision of Papert and collaborators and their use of programmable robotics with children [12], eventually leading to multiple commercial realizations (LEGO mindstorm, Arduino, etc.), a large public following, and a major role in education both in the classroom and through competitions such as First Robotics [1]. We also see additional potential for integrating more creative and artistic aspects into STEM, i.e., leading to generalized Science, Technology, Engineering, Arts, and Mathematics (STEAM) disciplines [13]. We invite others to join us in these endeavors—all instructional materials are available in the appendix for further adaptations and educational use.     

Double dissociation of PKC and AC manipulations on operant and classical learning in Drosophila

Learning about relationships between stimuli (i.e., classical conditioning [1]) and learning about consequences of one's own behavior (i.e., operant conditioning [2]) constitute the major part of our predictive understanding of the world. Since these forms of learning were recognized as two separate types 80 years ago [3], a recurrent concern has been the issue of whether one biological process can account for both of them [4, 5, 6, 7, 8, 9]. Today, we know the anatomical structures required for successful learning in several different paradigms, e.g., operant and classical processes can be localized to different brain regions in rodents [9] and an identified neuron in Aplysia shows opposite biophysical changes after operant and classical training, respectively [5]. We also know to some detail the molecular mechanisms underlying some forms of learning and memory consolidation. However, it is not known whether operant and classical learning can be distinguished at the molecular level. Therefore, we investigated whether genetic manipulations could differentiate between operant and classical learning in Drosophila. We found a double dissociation of protein kinase C and adenylyl cyclase on operant and classical learning. Moreover, the two learning systems interacted hierarchically such that classical predictors were learned preferentially over operant predictors.     

Functional Brain Networks Develop from a “Local to Distributed” Organization

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.     

Asymmetric Evolutionary Games

Evolutionary game theory is a powerful framework for studying evolution in populations of interacting individuals. A common assumption in evolutionary game theory is that interactions are symmetric, which means that the players are distinguished by only their strategies. In nature, however, the microscopic interactions between players are nearly always asymmetric due to environmental effects, differing baseline characteristics, and other possible sources of heterogeneity. To model these phenomena, we introduce into evolutionary game theory two broad classes of asymmetric interactions: ecological and genotypic. Ecological asymmetry results from variation in the environments of the players, while genotypic asymmetry is a consequence of the players having differing baseline genotypes. We develop a theory of these forms of asymmetry for games in structured populations and use the classical social dilemmas, the Prisoner’s Dilemma and the Snowdrift Game, for illustrations. Interestingly, asymmetric games reveal essential differences between models of genetic evolution based on reproduction and models of cultural evolution based on imitation that are not apparent in symmetric games.     

Grass Gazers: Using citizen science as a tool to facilitate practical and online science learning for secondary school students during the COVID‐19 lockdown

The coronavirus disease of 2019 (COVID‐19) pandemic has impacted educational systems worldwide during 2020, including primary and secondary schooling. To enable students of a local secondary school in Brisbane, Queensland, to continue with their practical agricultural science learning and facilitate online learning, a “Grass Gazers” citizen science scoping project was designed and rapidly implemented as a collaboration between the school and a multidisciplinary university research group focused on pollen allergy. Here, we reflect on the process of developing and implementing this project from the perspective of the school and the university. A learning package including modules on pollen identification, tracking grass species, measuring field greenness, using a citizen science data entry platform, forensic palynology, as well as video guides, risk assessment and feedback forms were generated. Junior agriculture science students participated in the learning via online lessons and independent data collection in their own local neighborhood and/or school grounds situated within urban environments. The university research group and school coordinator, operating in their own distributed work environments, had to develop, source, adopt, and/or adapt material rapidly to meet the unique requirements of the project. The experience allowed two‐way knowledge exchange between the secondary and tertiary education sectors. Participating students were introduced to real‐world research and were able to engage in outdoor learning during a time when online, indoor, desk‐based learning dominated their studies. The unique context of restrictions imposed by the social isolation policies, as well as government Public Health and Department of Education directives, allowed the team to respond by adapting teaching and research activity to develop and trial learning modules and citizen science tools. The project provided a focus to motivate and connect teachers, academic staff, and school students during a difficult circumstance. Extension of this citizen project for the purposes of research and secondary school learning has the potential to offer ongoing benefits for grassland ecology data acquisition and student exposure to real‐world science.     

Functional Brain Networks Develop from a “Local to Distributed” Organization

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward ‘segregation’ (a general decrease in correlation strength) between regions close in anatomical space and ‘integration’ (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more “distributed” architecture in young adults. We argue that this “local to distributed” developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing “small-world”-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.