16,534 research outputs found

    Consensus through Herding

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    State Machine Replication (SMR) is an important abstraction for a set of nodes to agree on an ever-growing, linearly-ordered log of transactions. In decentralized cryptocurrency applications, we would like to design SMR protocols that 1) resist adaptive corruptions; and 2) achieve small bandwidth and small confirmation time. All past approaches towards constructing SMR fail to achieve either small confirmation time or small bandwidth under adaptive corruptions (without resorting to strong assumptions such as the erasure model or proof-of-work). We propose a novel paradigm for reaching consensus that departs significantly from classical approaches. Our protocol is inspired by a social phenomenon called herding, where people tend to make choices considered as the social norm. In our consensus protocol, leader election and voting are coalesced into a single (randomized) process: in every round, every node tries to cast a vote for what it views as the {\it most popular} item so far: such a voting attempt is not always successful, but rather, successful with a certain probability. Importantly, the probability that the node is elected to vote for vv is independent from the probability it is elected to vote for v2˘7≠vv\u27 \neq v. We will show how to realize such a distributed, randomized election process using appropriate, adaptively secure cryptographic building blocks. We show that amazingly, not only can this new paradigm achieve consensus (e.g., on a batch of unconfirmed transactions in a cryptocurrency system), but it also allows us to derive the first SMR protocol which, even under adaptive corruptions, requires only polylogarithmically many rounds and polylogarithmically many honest messages to be multicast to confirm each batch of transactions; and importantly, we attain these guarantees under standard cryptographic assumptions

    Herding and Social Pressure in Trading Tasks: A Behavioural Analysis

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    We extend the experimental literature on Bayesian herding using evidence from a financial decision-making experiment. We identify significant propensities to herd increasing with the degree of herd-consensus. We test various herding models to capture the differential impacts of Bayesian-style thinking versus behavioural factors. We find statistically significant associations between herding and individual characteristics such as age and personality traits. Overall, our evidence is consistent with explanations of herding as the outcome of social and behavioural factors. Suggestions for further research are outlined and include verifying these findings and identifying the neurological correlates of propensities to herd

    The noisy voter model under the influence of contrarians

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    The influence of contrarians on the noisy voter model is studied at the mean-field level. The noisy voter model is a variant of the voter model where agents can adopt two opinions, optimistic or pessimistic, and can change them by means of an imitation (herding) and an intrinsic (noise) mechanisms. An ensemble of noisy voters undergoes a finite-size phase transition, upon increasing the relative importance of the noise to the herding, form a bimodal phase where most of the agents shear the same opinion to a unimodal phase where almost the same fraction of agent are in opposite states. By the inclusion of contrarians we allow for some voters to adopt the opposite opinion of other agents (anti-herding). We first consider the case of only contrarians and show that the only possible steady state is the unimodal one. More generally, when voters and contrarians are present, we show that the bimodal-unimodal transition of the noisy voter model prevails only if the number of contrarians in the system is smaller than four, and their characteristic rates are small enough. For the number of contrarians bigger or equal to four, the voters and the contrarians can be seen only in the unimodal phase. Moreover, if the number of voters and contrarians, as well as the noise and herding rates, are of the same order, then the probability functions of the steady state are very well approximated by the Gaussian distribution

    Markets, herding and response to external information

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    We focus on the influence of external sources of information upon financial markets. In particular, we develop a stochastic agent-based market model characterized by a certain herding behavior as well as allowing traders to be influenced by an external dynamic signal of information. This signal can be interpreted as a time-varying advertising, public perception or rumor, in favor or against one of two possible trading behaviors, thus breaking the symmetry of the system and acting as a continuously varying exogenous shock. As an illustration, we use a well-known German Indicator of Economic Sentiment as information input and compare our results with Germany's leading stock market index, the DAX, in order to calibrate some of the model parameters. We study the conditions for the ensemble of agents to more accurately follow the information input signal. The response of the system to the external information is maximal for an intermediate range of values of a market parameter, suggesting the existence of three different market regimes: amplification, precise assimilation and undervaluation of incoming information.Comment: 30 pages, 8 figures. Thoroughly revised and updated version of arXiv:1302.647

    Why can’t professional macroeconomic forecasters predict recessions?

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    The professional forecasters’ inability to anticipate macroeconomic recessions is well documented. The literature has found that aggregate or consensus forecasts are too optimistic before downturns and too pessimistic before recoveries. This paper explores whether this result also holds with individual data. Using a Spanish survey of professional forecasters conducted by Funcas, I find that forecasters are indeed too optimistic before recessions for two reasons. First, strong herding behaviour around the consensus forecast prevents those forecasters perceiving the early signs of a recession from adjusting their expectations as much as needed to predict it. And second, some forecasters put too much weight on the most recent developments when producing their forecasts and fail to fully account for the reversion to the mean embedded in the data-generating process. Both factors lead to negative forecast errors when a recession occurs. Consequently, professional forecasters could improve their forecasting performance by placing less weight on indicators from the recent past and by avoiding inefficient herding
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