Intellectual Property and the Prisoner’s Dilemma: A Game Theory Justification of Copyrights, Patents, and Trade Secrets

In this article, I will offer an argument for the protection of intellectual property based on individual self-interest and prudence. In large part, this argument will parallel considerations that arise in a prisoner’s dilemma game. In brief, allowing content to be unprotected in terms of free access leads to a sub-optimal outcome where creation and innovation are suppressed. Adopting the institutions of copyright, patent, and trade secret is one way to avoid these sub-optimal results

Structure emerges faster during cultural transmission in children than in adults

How does children’s limited processing capacity affect cultural transmission of complex information? We show that over the course of iterated reproduction of two-dimensional random dot patterns transmission accuracy increased to a similar extent in 5- to 8-year-old children and adults whereas algorithmic complexity decreased faster in children. Thus, children require more structure to render complex inputs learnable. In line with the Less-Is-More hypothesis, we interpret this as evidence that children’s processing limitations affecting working memory capacity and executive control constrain the ability to represent and generate complexity, which, in turn, facilitates emergence of structure. This underscores the importance of investigating the role of children in the transmission of complex cultural traits

Cultural transmission results in convergence towards colour term universals.

As in biological evolution, multiple forces are involved in cultural evolution. One force is analogous to selection, and acts on differences in the fitness of aspects of culture by influencing who people choose to learn from. Another force is analogous to mutation, and influences how culture changes over time owing to errors in learning and the effects of cognitive biases. Which of these forces need to be appealed to in explaining any particular aspect of human cultures is an open question. We present a study that explores this question empirically, examining the role that the cognitive biases that influence cultural transmission might play in universals of colour naming. In a large-scale laboratory experiment, participants were shown labelled examples from novel artificial systems of colour terms and were asked to classify other colours on the basis of those examples. The responses of each participant were used to generate the examples seen by subsequent participants. By simulating cultural transmission in the laboratory, we were able to isolate a single evolutionary force-the effects of cognitive biases, analogous to mutation-and examine its consequences. Our results show that this process produces convergence towards systems of colour terms similar to those seen across human languages, providing support for the conclusion that the effects of cognitive biases, brought out through cultural transmission, can account for universals in colour naming

Agent Based Modeling of Electronic Markets To Analyze the Sustainability of Mutual Cooperation

According to game theory, dominant strategy of Prisoner’s Dilemma game is defecting. Since online trading between two strangers falls in the realm of a Prisoner’s Dilemma, failure should be the only logical conclusion of such electronic commerce situation. Traders are less likely to cooperate since they are not required to deal with the same buyer again given the enormous population of online traders. Thus one could argue that markets like eBay should never exist. Then what is the reason behind resounding success of such electronic markets? The answer lies in the reputation system that they established. Google page rankings and reader reviews on various sites like Amazon.com and Epinions.com are some other examples of the reputation system pointing towards the enormous influence that they wield in our everyday life. This research thus attempts to analyze importance of reputation system in an online trading using Agent Based Simulation. The effect of reputation system on the sustainability of mutual cooperation between online traders is analyzed by taking into account key factors like level of gullibility of online traders and the weight of influence given to their past behavior. Results indicate that reputation system is certainly capable of sustaining cooperation among online traders. However, weightage given to the past behavior of agents should be decided only after taking into account the level of gullibility of agents. Use of agent based modeling for studying dynamics of online trading is a relatively new concept. Sustaining cooperation among online traders is often perceived to be difficult task. The reputation system proposed in this research is expected to make contribution towards this goal

Citizen Social Lab: A digital platform for human behaviour experimentation within a citizen science framework

Cooperation is one of the behavioral traits that define human beings, however we are still trying to understand why humans cooperate. Behavioral experiments have been largely conducted to shed light into the mechanisms behind cooperation and other behavioral traits. However, most of these experiments have been conducted in laboratories with highly controlled experimental protocols but with varied limitations which limits the reproducibility and the generalization of the results obtained. In an attempt to overcome these limitations, some experimental approaches have moved human behavior experimentation from laboratories to public spaces, where behaviors occur naturally, and have opened the participation to the general public within the citizen science framework. Given the open nature of these environments, it is critical to establish the appropriate protocols to maintain the same data quality that one can obtain in the laboratories. Here, we introduce Citizen Social Lab, a software platform designed to be used in the wild using citizen science practices. The platform allows researchers to collect data in a more realistic context while maintaining the scientific rigour, and it is structured in a modular and scalable way so it can also be easily adapted for online or brick-and-mortar experimental laboratories. Following citizen science guidelines, the platform is designed to motivate a more general population into participation, but also to promote engaging and learning of the scientific research process. We also review the main results of the experiments performed using the platform up to now, and the set of games that each experiment includes. Finally, we evaluate some properties of the platform, such as the heterogeneity of the samples of the experiments and their satisfaction level, and the parameters that demonstrate the robustness of the platform and the quality of the data collected.Comment: 17 pages, 11 figures and 4 table

Agent-based Simulation of Online Trading

AbstractIt is evident that sustained cooperation among online traders is absolutely essential for ensuring the success of electronic markets. This research tries to explore the underlying relationship between reputation engineering system and cooperation level by employing ‘Agent Based Simulation Modeling’ approach. It attempts to establish a trust based reputation system and analyze its effect on the sustainability of mutual cooperation between online traders by taking into account key factors such as level of gullibility of online traders and the weight of influence given to their past behavior. The simulation result reveals the correlation between the Smoothing Constant and the Probability of Imitation. The maximum permissible probability of imitation to maintain full cooperation decreases with the increase in the smoothing constant. The mean trader profit decreases as the smoothing constant increases

Percolation and depinning transitions in cut-and-paste models of adaptation

We show that a cut-and-paste model to mimic a trial-and-error process of adaptation displays two pairs of percolation and depinning transitions, one for persistence and the other for efficiency. The percolation transition signals the onset of a property and the depinning transition, the growth of the same property. Despite its simplicity, the cut-and-paste model is qualitatively the same as the Minority Game. A majority cut-and-paste model is also introduced, to mimic the spread of a trend. When both models are iterated, the majority model reaches a frozen state while the minority model converges towards an alternate state. We show that a transition from the frozen to the alternate state occurs in the limit of a non-adaptive system

On iterated learning for task-oriented dialogue

Dans le traitement de langue et des système de dialogue, il est courant de pré-entraîner des modèles de langue sur corpus humain avant de les affiner par le biais d'un simulateur et de résolution de tâches. Malheuresement, ce type d'entrainement tend aussi à induire un phénomène connu sous le nom de dérive du langage. Concrétement, les propriétés syntaxiques et sémantiques de la langue intiallement apprise se détériorent: les agents se concentrent uniquement sur la résolution de la tâche, et non plus sur la préservation de la langue. En s'inspirant des travaux en sciences cognitives, et notamment l'apprentigssage itératif Kirby and Griffiths (2014), nous proposons ici une approche générique pour contrer cette dérive du langage. Nous avons appelé cette méthode Seeded iterated learning (SIL), ou apprentissage itératif capitalisé. Ce travail a été publié sous le titre (Lu et al., 2020b) et est présenté au chapitre 2. Afin d'émuler la transmission de la langue entre chaque génération d'agents, un agent étudiant est d'abord pré-entrainé avant d'être affiné de manière itérative, et ceci, en imitant des données échantillonnées à partir d'un agent enseignant nouvellement formé. À chaque génération, l'enseignant est créé en copiant l'agent étudiant, avant d'être de nouveau affiné en maximisant le taux de réussite de la tâche sous-jacente. Dans un second temps, nous présentons Supervised Seeded iterated learning (SSIL) dans le chapitre 3, où apprentissage itératif capitalisé avec supervision, qui a été publié sous le titre (Lu et al., 2020b). SSIL s'appuie sur SIL en le combinant avec une autre méthode populaire appelée Supervised SelfPlay (S2P) (Gupta et al., 2019), où apprentissage supervisé par auto-jeu. SSIL est capable d'atténuer les problèmes de S2P et de SIL, i.e. la dérive du langage dans les dernier stades de l'entrainement tout en préservant une plus grande diversité linguistique. Tout d'abord, nous évaluons nos méthodes dans sous la forme d'une preuve de concept à traver le Jeu de Lewis avec du langage synthetique. Dans un second temps, nous l'étendons à un jeu de traduction se utilisant du langage naturel. Dans les deux cas, nous soulignons l'efficacité de nos méthodes par rapport aux autres méthodes de la litterature. Dans le chapitre 1, nous discutons des concepts de base nécessaires à la compréhension des articles présentés dans les chapitres 2 et 3. Nous décrivons le problème spécifique du dialogue orienté tâche, y compris les approches actuelles et les défis auxquels ils sont confrontés : en particulier, la dérive linguistique. Nous donnons également un aperçu du cadre d'apprentissage itéré. Certaines sections du chapitre 1 sont empruntées aux articles pour des raisons de cohérence et de facilité de compréhension. Le chapitre 2 comprend les travaux publiés sous le nom de (Lu et al., 2020b) et le chapitre 3 comprend les travaux publiés sous le nom de (Lu et al., 2020a), avant de conclure au chapitre 4.In task-oriented dialogue, pretraining on human corpus followed by finetuning in a simulator using selfplay suffers from a phenomenon called language drift. The syntactic and semantic properties of the learned language deteriorates as the agents only focuses on solving the task. Inspired by the iterative learning framework in cognitive science Kirby and Griffiths (2014), we propose a generic approach to counter language drift called Seeded iterated learning (SIL). This work was published as (Lu et al., 2020b) and is presented in Chapter 2. In an attempt to emulate transmission of language between generations, a pretrained student agent is iteratively refined by imitating data sampled from a newly trained teacher agent. At each generation, the teacher is created by copying the student agent, before being finetuned to maximize task completion.We further introduce Supervised Seeded iterated learning (SSIL) in Chapter 3, work which was published as (Lu et al., 2020a). SSIL builds upon SIL by combining it with the other popular method called Supervised SelfPlay (S2P) (Gupta et al., 2019). SSIL is able to mitigate the problems of both S2P and SIL namely late-stage training collapse and low language diversity. We evaluate our methods in a toy setting of Lewis Game, and then scale it up to the translation game with natural language. In both settings, we highlight the efficacy of our methods compared to the baselines. In Chapter 1, we talk about the core concepts required for understanding the papers presented in Chapters 2 and 3. We describe the specific problem of task-oriented dialogue including current approaches and the challenges they face: particularly, the challenge of language drift. We also give an overview of the iterated learning framework. Some sections in Chapter 1 are borrowed from the papers for coherence and ease of understanding. Chapter 2 comprises of the work published as (Lu et al., 2020b) and Chapter 3 comprises of the work published as (Lu et al., 2020a). Chapter 4 gives a conclusion on the work