3,518 research outputs found
Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
It is widely believed that one's peers influence product adoption behaviors.
This relationship has been linked to the number of signals a decision-maker
receives in a social network. But it is unclear if these same principles hold
when the pattern by which it receives these signals vary and when peer
influence is directed towards choices which are not optimal. To investigate
that, we manipulate social signal exposure in an online controlled experiment
using a game with human participants. Each participant in the game makes a
decision among choices with differing utilities. We observe the following: (1)
even in the presence of monetary risks and previously acquired knowledge of the
choices, decision-makers tend to deviate from the obvious optimal decision when
their peers make similar decision which we call the influence decision, (2)
when the quantity of social signals vary over time, the forwarding probability
of the influence decision and therefore being responsive to social influence
does not necessarily correlate proportionally to the absolute quantity of
signals. To better understand how these rules of peer influence could be used
in modeling applications of real world diffusion and in networked environments,
we use our behavioral findings to simulate spreading dynamics in real world
case studies. We specifically try to see how cumulative influence plays out in
the presence of user uncertainty and measure its outcome on rumor diffusion,
which we model as an example of sub-optimal choice diffusion. Together, our
simulation results indicate that sequential peer effects from the influence
decision overcomes individual uncertainty to guide faster rumor diffusion over
time. However, when the rate of diffusion is slow in the beginning, user
uncertainty can have a substantial role compared to peer influence in deciding
the adoption trajectory of a piece of questionable information
Modeling and improving Spatial Data Infrastructure (SDI)
Spatial Data Infrastructure (SDI) development is widely known to be a challenging process owing to its complex and dynamic nature. Although great effort has been made to conceptually explain the complexity and dynamics of SDIs, few studies thus far have actually modeled these complexities. In fact, better modeling of SDI complexities will lead to more reliable plans for its development. A state-of-the-art simulation model of SDI development, hereafter referred to as SMSDI, was created by using the system dynamics (SD) technique. The SMSDI enables policy-makers to test various investment scenarios in different aspects of SDI and helps them to determine the optimum policy for further development of an SDI. This thesis begins with adaption of the SMSDI to a new case study in Tanzania by using the community of participant concept, and further development of the model is performed by using fuzzy logic. It is argued that the techniques and models proposed in this part of the study enable SDI planning to be conducted in a more reliable manner, which facilitates receiving the support of stakeholders for the development of SDI.Developing a collaborative platform such as SDI would highlight the differences among stakeholders including the heterogeneous data they produce and share. This makes the reuse of spatial data difficult mainly because the shared data need to be integrated with other datasets and used in applications that differ from those originally produced for. The integration of authoritative data and Volunteered Geographic Information (VGI), which has a lower level structure and production standards, is a new, challenging area. The second part of this study focuses on proposing techniques to improve the matching and integration of spatial datasets. It is shown that the proposed solutions, which are based on pattern recognition and ontology, can considerably improve the integration of spatial data in SDIs and enable the reuse or multipurpose usage of available data resources
Complexity of Government response to Covid-19 pandemic: A perspective of coupled dynamics on information heterogeneity and epidemic outbreak
This study aims at modeling the universal failure in preventing the outbreak
of COVID-19 via real-world data from the perspective of complexity and network
science. Through formalizing information heterogeneity and government
intervention in the coupled dynamics of epidemic and infodemic spreading;
first, we find that information heterogeneity and its induced variation in
human responses significantly increase the complexity of the government
intervention decision. The complexity results in a dilemma between the socially
optimal intervention that is risky for the government and the privately optimal
intervention that is safer for the government but harmful to the social
welfare. Second, via counterfactual analysis against the COVID-19 crisis in
Wuhan, 2020, we find that the intervention dilemma becomes even worse if the
initial decision time and the decision horizon vary. In the short horizon, both
socially and privately optimal interventions agree with each other and require
blocking the spread of all COVID-19-related information, leading to a
negligible infection ratio 30 days after the initial reporting time. However,
if the time horizon is prolonged to 180 days, only the privately optimal
intervention requires information blocking, which would induce a
catastrophically higher infection ratio than that in the counter-factual world
where the socially optimal intervention encourages early-stage information
spread. These findings contribute to the literature by revealing the complexity
incurred by the coupled infodemic-epidemic dynamics and information
heterogeneity to the governmental intervention decision, which also sheds
insight into the design of an effective early warning system against the
epidemic crisis in the future.Comment: This version contains the full-resolution figures for the paper DOI:
10.1007/s11071-023-08427-
What drives the market value of firms in the Defense industry ?
This paper investigates the relative importance of different types of news in driving significant stock price changes of firms in the defense industry. We implement a systematic event study with a sample of the 58 largest publicly listed companies in the defense industry, over the time period 1995-2005. We first identify, for each firm, the statistically significant abnormal returns over the time period, and then we look for information releases likely to cause such stock price movements. We find that stock price movements in the defense industry are, in many ways, influenced by the same events as in other industries (key role of formal earnings announcements or analysts' recommendations) but this industry also has some specific features, in particular the influence of geopolitical events and the relevance and frequency of bids and contracts on stock prices.Event study, financial markets, defense industry, information releases, GARCH models.
- âŠ