Yaneer Bar-Yam

Yaneer Bar-Yam, Jean Langlois-Meurinne, Mari Kawakatsu et al.

We consider the relationship between economic activity and intervention, including monetary and fiscal policy, using a universal dynamic framework. Central bank policies are designed for growth without excess inflation. However, unemployment, investment, consumption, and inflation are interlinked. Understanding dynamics is crucial to assessing the effects of policy, especially in the aftermath of the financial crisis. Here we lay out a program of research into monetary and economic dynamics and preliminary steps toward its execution. We use principles of response theory to derive implications for policy. We find that the current approach, which considers the overall money supply, is insufficient to regulate economic growth. While it can achieve some degree of control, optimizing growth also requires a fiscal policy balancing monetary injection between two dominant loop flows, the consumption and wages loop, and investment and returns loop. The balance arises from a composite of government tax, entitlement, subsidy policies, corporate policies, as well as monetary policy. We show empirically that a transition occurred in 1980 between two regimes--an oversupply to the consumption and wages loop, to an oversupply of the investment and returns loop. The imbalance is manifest in savings and borrowing by consumers and investors, and in inflation. The latter increased until 1980, and decreased subsequently, resulting in a zero rate largely unrelated to the financial crisis. Three recessions and the financial crisis are part of this dynamic. Optimizing growth now requires shifting the balance. Our analysis supports advocates of greater income and / or government support for the poor who use a larger fraction of income for consumption. This promotes investment due to growth in demand. Otherwise, investment opportunities are limited, capital remains uninvested, and does not contribute to growth.

Egemen Sert, Yaneer Bar-Yam, Alfredo J. Morales

Societies are complex. Properties of social systems can be explained by the interplay and weaving of individual actions. Incentives are key to understand people's choices and decisions. For instance, individual preferences of where to live may lead to the emergence of social segregation. In this paper, we combine Reinforcement Learning (RL) with Agent Based Models (ABM) in order to address the self-organizing dynamics of social segregation and explore the space of possibilities that emerge from considering different types of incentives. Our model promotes the creation of interdependencies and interactions among multiple agents of two different kinds that want to segregate from each other. For this purpose, agents use Deep Q-Networks to make decisions based on the rules of the Schelling Segregation model and the Predator-Prey model. Despite the segregation incentive, our experiments show that spatial integration can be achieved by establishing interdependencies among agents of different kinds. They also reveal that segregated areas are more probable to host older people than diverse areas, which attract younger ones. Through this work, we show that the combination of RL and ABMs can create an artificial environment for policy makers to observe potential and existing behaviors associated to incentives.

Vincent Wong, Yaneer Bar-Yam

Research from a variety of fields including psychology and linguistics have found correlations and patterns in personal attributes and behavior, but efforts to understand the broader heterogeneity in human behavior have not yet integrated these approaches and perspectives with a cohesive methodology. Here we extract patterns in behavior and relate those patterns together in a high-dimensional picture. We use dimension reduction to analyze word usage in text data from the online discussion platform Reddit. We find that pronouns can be used to characterize the space of the two most prominent dimensions that capture the greatest differences in word usage, even though pronouns were not included in the determination of those dimensions. These patterns overlap with patterns of topics of discussion to reveal relationships between pronouns and topics that can describe the user population. This analysis corroborates findings from past research that have identified word use differences across populations and synthesizes them relative to one another. We believe this is a step toward understanding how differences between people are related to each other.

Karla Z. Bertrand, Maya Bialik, Kawandeep Virdee et al.

Measuring public sentiment is a key task for researchers and policymakers alike. The explosion of available social media data allows for a more time-sensitive and geographically specific analysis than ever before. In this paper we analyze data from the micro-blogging site Twitter and generate a sentiment map of New York City. We develop a classifier specifically tuned for 140-character Twitter messages, or tweets, using key words, phrases and emoticons to determine the mood of each tweet. This method, combined with geotagging provided by users, enables us to gauge public sentiment on extremely fine-grained spatial and temporal scales. We find that public mood is generally highest in public parks and lowest at transportation hubs, and locate other areas of strong sentiment such as cemeteries, medical centers, a jail, and a sewage facility. Sentiment progressively improves with proximity to Times Square. Periodic patterns of sentiment fluctuate on both a daily and a weekly scale: more positive tweets are posted on weekends than on weekdays, with a daily peak in sentiment around midnight and a nadir between 9:00 a.m. and noon.

Blake C. Stacey, Yaneer Bar-Yam

Cybersecurity attacks are a major and increasing burden to economic and social systems globally. Here we analyze the principles of security in different domains and demonstrate an architectural flaw in current cybersecurity. Cybersecurity is inherently weak because it is missing the ability to defend the overall system instead of individual computers. The current architecture enables all nodes in the computer network to communicate transparently with one another, so security would require protecting every computer in the network from all possible attacks. In contrast, other systems depend on system-wide protections. In providing conventional security, police patrol neighborhoods and the military secures borders, rather than defending each individual household. Likewise, in biology, the immune system provides security against viruses and bacteria using primarily action at the skin, membranes, and blood, rather than requiring each cell to defend itself. We propose applying these same principles to address the cybersecurity challenge. This will require: (a) Enabling pervasive distribution of self-propagating securityware and creating a developer community for such securityware, and (b) Modifying the protocols of internet routers to accommodate adaptive security software that would regulate internet traffic. The analysis of the immune system architecture provides many other principles that should be applied to cybersecurity. Among these principles is a careful interplay of detection and action that includes evolutionary improvement. However, achieving significant security gains by applying these principles depends strongly on remedying the underlying architectural limitations.