This paper studies a simple model of observational learning where agents care not only about the information of others but also about their actions. We show that despite complex strategic considerations that arise from forward-looking incentives, herd behavior can arise in equilibrium. The model encompasses applications such as sequential elections, public good contributions, and leadership cha...

We consider a standard sequential decision to adopt/buy a good in a herding environment. The setup is same as in Sgroi (2002). Contrary to the basic herding case we introduce a cost that the agents have to pay for the information about their predecessors’ actions. All agents receive informative signals as in the standard herding models but do not view the actions taken by their predecessors unl...

The aim of this study is to analyze herding behavior in the Indonesian capital market as an emerging which has experienced high JCI volatility throughout 2020. analysis takes place three periods: (a) 2020 period with 242 observation dates (233 after outliers were removed), (b) ) bearish (down) conditions 59 (51 and (c) bullish (rising) 183 dates. sample consisted 36 companies taken from members...

The conjecture that investor sentiment leads important groups of investors to act similarly and thereby affect prices is an important ingredient of models of noise trading and style investing. In contrast to Lakonishok et al. (1992), who find only weak evidence of herding among institutional investors and conjecture that retail investors will herd even less, we document that a sample of over 30...

We introduce a new family of online learning algorithms based upon constraining the velocity flow over a distribution of weight vectors. In particular, we show how to effectively herd a Gaussian weight vector distribution by trading off velocity constraints with a loss function. By uniformly bounding this loss function, we demonstrate how to solve the resulting optimization analytically. We com...

This paper outlines and contextualises some current findings of an on-going investigation begun in 2009 at the Visual Effects Research Lab (VERL). The three-year project links the worlds of film, art, technology and computer science. In sharing methodologies and promoting cross-, transand inter-disciplinary understanding the project is beginning to challenge established notions of visual though...

In social-learning environments, we investigate implications of the assumption that people naively believe that each previous person’s action reflects solely that person’s private information, leading them to systematically imitate all predecessors even in the many circumstances where rational agents do not. Naive herders inadvertently over-weight early movers’ private signals by neglecting tha...

Shepherding with a dog presents an interesting challenge for artificial intelligence, with multiple intelligent systems assessing and interacting with each other in order to achieve a variety of goals. We present a solution to this problem, which consists of a dog AI making use of influence mapping, state machines and A* pathfinding to respond intelligently to real-life shepherding commands iss...

The Herding algorithm is an algorithm of recent interest in the machine learning community, motivated by inference in Markov random fields. It solves the following sampling problem: given a set X ⊂ R with mean μ, construct an infinite sequence of points from X such that, for every t ≥ 1, the mean of the first t points in that sequence lies within Euclidean distance O(1/t) of μ. The classic Perc...

Numerous real life problems represents hard optimization problems that cannot be solved by deterministic algorithm. In the past decades various different methods were proposed for these kind of problems and one of the methods are nature inspired algorithms especially swarm intelligence algorithms. Elephant herding optimization algorithm (EHO) is one of the recent swarm intelligence algorithm th...