The Bad Thing About Good Advice: Understanding When and How Advice Exacerbates Overconfidence

Much research on advice taking examines how people revise point estimates given input from others. This work has established that people often egocentrically discount advice. If they were to place more weight on advice, their point estimates would be more accurate. Yet the focus on point estimates and accuracy has resulted in a narrow conception of what it means to heed advice. We distinguish between revisions of point estimates and revisions of attendant probability distributions. Point estimates represent a single best guess; distributions represent the probabilities that people assign to all possible answers. A more complete picture of advice taking is provided by considering revisions of distributions, which reflect changes in both confidence and best guesses. We capture this using a new measure of advice utilization: the influence of advice. We observe that, when input from a high-quality advisor largely agrees with a person’s initial opinion, it engenders little change in one’s point estimate and, hence, little change in accuracy yet significantly increases confidence. This pattern suggests more advice taking than generally suspected. However, it is not necessarily beneficial. Because people are typically overconfident to begin with, receiving advice that agrees with their initial opinion can exacerbate overconfidence. In several experiments, we manipulate advisor quality and measure the extent to which advice agrees with a person’s initial opinion. The results allow us to pinpoint circumstances in which heeding advice is beneficial, improving accuracy or reducing overconfidence, as well as circumstances in which it is harmful, hurting accuracy or exacerbating overconfidence. This paper was accepted by Yuval Rottenstreich, judgment and decision making.

Opinion Dynamics over a Finite Set in Cooperative Multi-robot Systems: An Asynchronous Gossip-Based Consensus Approach

The focus of this paper is to present an algorithm that allows robotic teams to make decisions between a finite set of choices. The approach used was based on models that represent the way groups of humans evolve their opinions through time. Numerous works have explored models that consider the opinion as continuous values, while the literature less frequently considers groups trying to reach an agreement when only a finite set of possible opinions is given. The main contribution of this paper is to present a consensus algorithm that can be applied in those scenarios. For this purpose, it is briefly reviewed some crucial concepts for the definition of the proposed algorithm, which is based on asynchronous gossip. Due to the stochasticity of this approach, it is not possible to precisely predict the behavior of the network. However, the results from both computational and laboratory experiments indicate the eigenvector centrality score as a valuable metric to predict the probability of an initial opinion to become the prevailing one for the group when they reach consensus. Also, the asynchrony of the proposed algorithm made it possible to reach consensus in scenarios where synchronous approaches could not.

Biased Opinion Dynamics: When the Devil is in the Details

We investigate opinion dynamics in multi-agent networks when there exists a bias toward one of two possible opinions; for example, reflecting a status quo vs a superior alternative. Starting with all agents sharing an initial opinion representing the status quo, the system evolves in steps. In each step, one agent selected uniformly at random adopts with some probability a the superior opinion, and with probability 1 - a it follows an underlying update rule to revise its opinion on the basis of those held by its neighbors. We analyze the convergence of the resulting process under two well-known update rules, namely majority and voter. The framework we propose exhibits a rich structure, with a nonobvious interplay between topology and underlying update rule. For example, for the voter rule we show that the speed of convergence bears no significant dependence on the underlying topology, whereas the picture changes completely under the majority rule, where network density negatively affects convergence. We believe that the model we propose is at the same time simple, rich, and modular, affording mathematical characterization of the interplay between bias, underlying opinion dynamics, and social structure in a unified setting.

THE DYNAMIC EFFECTS OF ONLINE PRODUCT REVIEWS ON PURCHASE DECISIONS

Previous studies have demonstrated that online reviews play an important role in the purchase decision process. Though the effects of positive and negative reviews to consumers’ purchase decisions have been analyzed, they were examined statically and separately. In reality, online review community allows everyone to express and receive opinions and individuals can reexamine their opinions after receiving messages from others. The goal of this paper is to study how potential customers form their opinions dynamically under the effects of both positive and negative reviews using a numerical simulation. The results show that consumers with different membership levels have different information sensitivities to online reviews. Consumers at low and medium membership levels are often persuaded by online reviews, regardless of their initial opinion about a product. On the other hand, online reviews have less effect on consumers at higher membership levels, who often make purchase decisions based on their initial impressions of a product.

Opinion Word Expansion and Target Extraction through Double Propagation

Analysis of opinions, known as opinion mining or sentiment analysis, has attracted a great deal of attention recently due to many practical applications and challenging research problems. In this article, we study two important problems, namely, opinion lexicon expansion and opinion target extraction. Opinion targets (targets, for short) are entities and their attributes on which opinions have been expressed. To perform the tasks, we found that there are several syntactic relations that link opinion words and targets. These relations can be identified using a dependency parser and then utilized to expand the initial opinion lexicon and to extract targets. This proposed method is based on bootstrapping. We call it double propagation as it propagates information between opinion words and targets. A key advantage of the proposed method is that it only needs an initial opinion lexicon to start the bootstrapping process. Thus, the method is semi-supervised due to the use of opinion word seeds. In evaluation, we compare the proposed method with several state-of-the-art methods using a standard product review test collection. The results show that our approach outperforms these existing methods significantly.

THE KEY ROLE OF SHORTCUTS OF SMALL-WORLD NETWORK IN OPINION FORMATION

In this paper, we analyze the average magnetization and spatial correlation of opinion formation in small-world network and in the regular lattice. We construct NW small-world network through adding shortcuts on the regular lattice. With computer simulation, we find that there exists short- and long-range spatial correlation of the average magnetization m(t) in NW small-world network and the evolution trend of opinion is dependent on the initial opinion condition by comparing with that in regular lattice. On the other hand, we analyze the average magnetization m(t) using the time series analysis, and find the Hurst exponent H ≃ 0.5 in NW small-world network and H = 0 in regular lattice. All the results show the important role of shortcuts in the NW small-world network, which reflects some interesting aspects in our real society indirectly.

THE SZNAJD MODEL WITH TEAM WORK

In 2000, Sznajd-weron and Sznajd introduced a model for the simulation of a closed democratic community with a two-party system, and it is found that a closed community has to evolve either to a dictatorship or a stalemate state. In this paper, we continued to study on this model. All the neighboring individuals holding the same opinion is defined as a team, which will influence its nearest neighbor's decision and realize the opinion evolution. After some time-steps, a steady state appeared and the stalemate state in original model is eliminated. Moreover, the demand of time-steps has decreased dramatically. In addition, we also analyzed the effect of the various dispersal degree of the initial opinion on the opinion converging at the probability of one steady state. Finally we analyzed the effect of noise on convergence and found that the ability of anti-noise was increased about 1000 times compared with Sznajd model.