Consensus formation on adaptive networks

The structure of a network can significantly influence the properties of the dynamical processes which take place on them. While many studies have been devoted to this influence, much less attention has been devoted to the interplay and feedback mechanisms between dynamical processes and network topology on adaptive networks. Adaptive rewiring of links can happen in real life systems such as acquaintance networks where people are more likely to maintain a social connection if their views and values are similar. In our study, we consider different variants of a model for consensus formation. Our investigations reveal that the adaptation of the network topology fosters cluster formation by enhancing communication between agents of similar opinion, though it also promotes the division of these clusters. The temporal behavior is also strongly affected by adaptivity: while, on static networks, it is influenced by percolation properties, on adaptive networks, both the early and late time evolution of the system are determined by the rewiring process. The investigation of a variant of the model reveals that the scenarios of transitions between consensus and polarized states are more robust on adaptive networks.Comment: 11 pages, 14 figure

Constraints on Earth system functioning at the Paleocene-Eocene Thermal Maximum from the marine silicon cycle

The Paleocene‐Eocene Thermal Maximum (PETM, ca. 56 Ma) is marked by a negative carbon isotope excursion (CIE) and increased global temperatures. The CIE is thought to result from the release of 13C‐depleted carbon, although the source(s) of carbon and triggers for its release, its rate of release, and the mechanisms by which the Earth system recovered are all debated. Many of the proposed mechanisms for the onset and recovery phases of the PETM make testable predictions about the marine silica cycle, making silicon isotope records a promising tool to address open questions about the PETM. We analyzed silicon isotope ratios (δ30Si) in radiolarian tests and sponge spicules from the Western North Atlantic (ODP Site 1051) across the PETM. Radiolarian δ30Si decreases by 0.6‰ from a background of 1‰ coeval with the CIE, while sponge δ30Si remains consistent at 0.2‰. Using a box model to test the Si cycle response to various scenarios, we find the data are best explained by a weak silicate weathering feedback, implying the recovery was mostly driven by nondiatom organic carbon burial, the other major long‐term carbon sink. We find no resolvable evidence for a volcanic trigger for carbon release, or for a change in regional oceanography. Better understanding of radiolarian Si isotope fractionation and more Si isotope records spanning the PETM are needed to confirm the global validity of these conclusions, but they highlight how the coupling between the silica and carbon cycles can be exploited to yield insight into the functioning of the Earth system

An Experimental Study of Cryptocurrency Market Dynamics

As cryptocurrencies gain popularity and credibility, marketplaces for cryptocurrencies are growing in importance. Understanding the dynamics of these markets can help to assess how viable the cryptocurrnency ecosystem is and how design choices affect market behavior. One existential threat to cryptocurrencies is dramatic fluctuations in traders' willingness to buy or sell. Using a novel experimental methodology, we conducted an online experiment to study how susceptible traders in these markets are to peer influence from trading behavior. We created bots that executed over one hundred thousand trades costing less than a penny each in 217 cryptocurrencies over the course of six months. We find that individual "buy" actions led to short-term increases in subsequent buy-side activity hundreds of times the size of our interventions. From a design perspective, we note that the design choices of the exchange we study may have promoted this and other peer influence effects, which highlights the potential social and economic impact of HCI in the design of digital institutions.Comment: CHI 201

Crises and collective socio-economic phenomena: simple models and challenges

Financial and economic history is strewn with bubbles and crashes, booms and busts, crises and upheavals of all sorts. Understanding the origin of these events is arguably one of the most important problems in economic theory. In this paper, we review recent efforts to include heterogeneities and interactions in models of decision. We argue that the Random Field Ising model (RFIM) indeed provides a unifying framework to account for many collective socio-economic phenomena that lead to sudden ruptures and crises. We discuss different models that can capture potentially destabilising self-referential feedback loops, induced either by herding, i.e. reference to peers, or trending, i.e. reference to the past, and account for some of the phenomenology missing in the standard models. We discuss some empirically testable predictions of these models, for example robust signatures of RFIM-like herding effects, or the logarithmic decay of spatial correlations of voting patterns. One of the most striking result, inspired by statistical physics methods, is that Adam Smith's invisible hand can badly fail at solving simple coordination problems. We also insist on the issue of time-scales, that can be extremely long in some cases, and prevent socially optimal equilibria to be reached. As a theoretical challenge, the study of so-called "detailed-balance" violating decision rules is needed to decide whether conclusions based on current models (that all assume detailed-balance) are indeed robust and generic.Comment: Review paper accepted for a special issue of J Stat Phys; several minor improvements along reviewers' comment

Information Revolutions and the Overthrow of Autocratic Regimes

This paper presents a model of information quality and political regime change. If enough citizens act against a regime, it is overthrown. Citizens are imperfectly informed about how hard this will be and the regime can, at a cost, engage in propaganda so that at face-value it seems hard. The citizens are rational and evaluate their information knowing the regime's incentives. The model makes three predictions. First, even rational citizens may not correctly infer the amount of manipulation. Second, as the intrinsic quality of information available becomes sufficiently high, the regime is more likely to survive. Third, the regime benefits from ambiguity about the amount of manipulation, and consequently, as it becomes cheaper to manipulate, the regime is also more likely to survive. Key results of the benchmark static model extend to a simple dynamic setting where there are waves of unrest