Why Do Birds False Alarm Flight?

False alarm flighting in avian flocks is common, and has been explained as a maladaptive information cascade. If false alarm flighting is maladaptive per se, then its frequency can only be explained by it being net adaptive in relation to some other benefit or equilibrium. However, I argue that natural selection cannot distinguish between false and true alarm flights that have similar energetic costs, opportunity costs, and outcomes. False alarm flighting cannot be maladaptive if natural selection cannot perceive the difference between true and false alarm flighting. Rather, the question to answer is what false and true alarm flighting both have in common that is adaptive per se. The fire drill hypothesis of alarm flighting posits that false alarm flights are an adaptive investment in practicing escape. The fire drill hypothesis predicts that all individuals can benefit from practicing escape, particularly juveniles. Flighting practice could improve recognition of and response time to alarm flighting signals, could compensate for inter-individual and within-day weight differences, and could aid the development of adaptive escape tactics. Mixed-age flocks with many juveniles are expected to false alarm flight more than adult flocks. Flocks that inhabit complex terrain should gain less from escape practice and should false alarm flight less. Behavioural ecology framings can be fruitfully complemented by other research traditions of learning and behaviour that are more focused on maturation and motor learning processes.

Effect of Regret Aversion and Information Cascade on Investment Decisions in the Real Estate Sector: The Mediating Role of Risk Perception and the Moderating Effect of Financial Literacy

The real estate sector plays a significant role in the economy of any country. However, many investors make irrational investments in the real estate market. Therefore, the purpose of this study is to assess the effects of regret aversion and information cascade on investment decisions while considering the moderating role of financial literacy and the mediating effect of risk perception in the real estate sector of developing countries. This research utilized a quantitative research technique, collecting data by distributing structured questionnaires to real estate investors, followed by convenience sampling. This study used both descriptive and inferential statistics to make the data more meaningful. SPSS 25.0 was utilized to interpret the data. Cronbach's alpha was used to test for internal consistency, while validity was checked through correlation. Confirmatory factor analysis (CFA) was applied to confirm that the items on the questionnaire are perfectly loaded on their construct. Furthermore, process macro, model 5, was used to investigate the moderation mediation. This work addresses a gap in the literature by studying financial literacy as a moderator and risk perception as a mediating variable in regret aversion bias and information cascade bias's relationships with investment decisions in the real estate sector. The results confirmed that financial literacy weakens the negative effect of behavioral biases (regret aversion and information cascade) on investment decisions. In addition, risk perception mediates the relationships between these cognitive biases (regret aversion and information cascade) and decision making. The effects of other behavioral biases in real estate and stock market contexts should be examined in future research.

Modelling the Latent Semantics of Diffusion Sources in Information Cascade Prediction

Predicting the information spread tendency can help products recommendation and public opinion management. The existing information cascade prediction models are devoted to extract the chronological features from diffusion sequences but treat the diffusion sources as ordinary users. Diffusion source, the first user in the information cascade, can indicate the latent topic and diffusion pattern of an information item to mine user potential common interests, which facilitates information cascade prediction. In this paper, for modelling the abundant implicit semantics of diffusion sources in information cascade prediction, we propose a Diffusion Source latent Semantics-Fused cascade prediction framework, named DSSF. Specifically, we firstly apply diffusion sources embedding to model the special role of the source users. To learn the latent interaction between users and diffusion sources, we proposed a co-attention-based fusion gate which fuses the diffusion sources’ latent semantics with user embedding. To address the challenge that the distribution of diffusion sources is long-tailed, we develop an adversarial training framework to transfer the semantics knowledge from head to tail sources. Finally, we conduct experiments on real-world datasets, and the results show that modelling the diffusion sources can significantly improve the prediction performance. Besides, this improvement is limited for the cascades from tail sources, and the adversarial framework can help.

Using Response Times to Infer Others’ Private Information: An Application to Information Cascades

The standard assumption in social learning environments is that agents learn from others through choice outcomes. We argue that in many settings, agents can also infer information from others’ response times (RT), which can increase efficiency. To investigate this, we conduct a standard information cascade experiment and find that RTs do contain information that is not revealed by choice outcomes alone. When RTs are observable, subjects extract this private information and are more likely to break from incorrect cascades. Our results suggest that in environments where RTs are publicly available, the information structure may be richer than previously thought. This paper was accepted by Yan Chen, decision analysis.

Why cannot long-term cascade be predicted? Exploring temporal dynamics in information diffusion processes

Predicting information cascade plays a crucial role in various applications such as advertising campaigns, emergency management and infodemic controlling. However, predicting the scale of an information cascade in the long-term could be difficult. In this study, we take Weibo, a Twitter-like online social platform, as an example, exhaustively extract predictive features from the data, and use a conventional machine learning algorithm to predict the information cascade scales. Specifically, we compare the predictive power (and the loss of it) of different categories of features in short-term and long-term prediction tasks. Among the features that describe the user following network, retweeting network, tweet content and early diffusion dynamics, we find that early diffusion dynamics are the most predictive ones in short-term prediction tasks but lose most of their predictive power in long-term tasks. In-depth analyses reveal two possible causes of such failure: the bursty nature of information diffusion and feature temporal drift over time. Our findings further enhance the comprehension of the information diffusion process and may assist in the control of such a process.

Modelling typing disfluencies as finite mixture process

To writing anything on a keyboard at all requires us to know first what to type, then to activate motor programmes for finger movements, and execute these. An interruption in the information flow at any of these stages leads to disfluencies. To capture this combination of fluent typing and typing hesitations, researchers calculate different measures from keystroke-latency data—such as mean inter-keystroke interval and pause frequencies. There are two fundamental problems with this: first, summary statistics ignore important information in the data and frequently result in biased estimates; second, pauses and pause-related measures are defined using threshold values which are, in principle, arbitrary. We implemented a series of Bayesian models that aimed to address both issues while providing reliable estimates for individual typing speed and statistically inferred process disfluencies. We tested these models on a random sample of 250 copy-task recordings. Our results illustrate that we can model copy typing as a mixture process of fluent and disfluent key transitions. We conclude that mixture models (1) map onto the information cascade that generate keystrokes, and (2) provide a principled approach to detect disfluencies in keyboard typing.