How Bad is Forming Your Own Opinion?

The question of how people form their opinion has fascinated economists and sociologists for quite some time. In many of the models, a group of people in a social network, each holding a numerical opinion, arrive at a shared opinion through repeated averaging with their neighbors in the network. Motivated by the observation that consensus is rarely reached in real opinion dynamics, we study a related sociological model in which individuals' intrinsic beliefs counterbalance the averaging process and yield a diversity of opinions. By interpreting the repeated averaging as best-response dynamics in an underlying game with natural payoffs, and the limit of the process as an equilibrium, we are able to study the cost of disagreement in these models relative to a social optimum. We provide a tight bound on the cost at equilibrium relative to the optimum; our analysis draws a connection between these agreement models and extremal problems that lead to generalized eigenvalues. We also consider a natural network design problem in this setting: which links can we add to the underlying network to reduce the cost of disagreement at equilibrium

Dynamics of hotel website browsing activity: the power of informatics and data analytics

© 2020, Emerald Publishing Limited. Purpose: This paper aims to investigate the temporal dynamics of users browsing activity on a hotel website in order to derive effective marketing strategies and constantly improve website effectiveness. Users' activities on the hotel's website on yearly, monthly, daily and hourly basis are examined and compared, demonstrating the power of informatics and data analytics. Design/methodology/approach: A total of 29,976 hourly Weblog files from 1 August 2014 to 31 December 2017 were collected from a luxury hotel in Hong Kong. ANOVA and post-hoc comparisons were used to analyse the data. Findings: Users' browsing behaviours, particularly stickiness, on the hotel website differ on yearly, monthly, daily and weekly bases. Users' activities increased steadily from 2014 to 2016, but dropped in 2017. Users are most active from July to September, on weekdays, and from noon to evening time. The month-, day-, and hour-based behaviours changed through years. The analysis of big data determines strategic and operational management and marketing decision-making. Research limitations/implications: Understanding the usage patterns of their websites allow organisations to make a range of strategic, marketing, pricing and distribution decisions to optimise their performance. Fluctuation of website usage and level of customer engagement have implications on customer support and services, as well as strategic partnership decisions. Originality/value: Leveraging the power of big data analytics, this paper adds to the existing literature by performing a comprehensive analysis on the temporal dynamics of users' online browsing behaviours

Optimizing web traffic via the media scheduling problem

Website traffic varies through time in consistent and predictable ways, with highest traffic in the middle of the day. When providing media content to visitors, it is important to present repeat visitors with new content so that they keep coming back. In this paper we present an algorithm to balance the need to keep a website fresh with new content with the desire to present the best content to the most visitors at times of peak traffic. We formulate this as the media scheduling problem, where we attempt to maximize total clicks, given the overall traffic pattern and the time varying click-through rates of available media content. We present an efficient algorithm to perform this scheduling under certain conditions and apply this algorithm to real data obtained from server logs, showing evidence of significant improvements in traffic from our algorithmic schedules. Finally, we analyze the click data, presenting models for why and how the click-through rate for new content declines as it ages