Honey bee as an alternative model invertebrate organism

Insects perfectly fit the flagship principle of animal research – 3R: to reduce (the number of animals), to replace (animals with alternative models) and to refine (methods). Bees have the most important advantages of a model organism: they cause minimal ethical controversy, they have a small and fully known genome, and they permit the use of many experimental techniques. Bees have a fully functional DNMT toolkit. Therefore, they are used as models in biomedical/genetic research, e.g. in research on the development of cancer or in the diagnostics of mental and neuroleptic diseases in humans. The reversion of aging processes in bees offers hope for progress in gerontology research. The cellular mechanisms of learning and memory coding, as well as the indicators of biochemical immunity parameters, are similar or analogous to those in humans, so bees may become useful in monitoring changes in behavior and metabolism. Bees are very well suited for studies on the dose of the substance applied to determine the lethal dose or the effect of a formula on life expectancy. Honeybees have proven to be an effective tool for studying the effects of a long-term consumption of stimulants, as well as for observing behavioral changes and developing addictions at the individual and social levels, as well as for investigating the effects of continuously delivering the same dose of a substance. The genomic and physiological flexibility of bees in dividing tasks among workers in a colony makes it possible to create a Single- Cohort Colony (SCC) in which peers compared perform different tasks. Moreover behavioral methods (e.g. Proboscis Extension Reflex – PER, Sting Extension Reflex – SER, free flying target discrimination tasks or the cap pushing response) make it possible to analyse changes occurring in honeybee brains during learning and remembering. Algorithms of actions are created based on the behavior of a colony or individual, e.g. Artificial Bee Colony Algorithm (ABCA). Honeybees are also model organisms for profiling the so-called intelligence of a swarm or collective intelligence. Additionally, they serve as models for guidance systems and aviation technologies. Bees have inspired important projects in robotics, such as B-droid, Robobee and The Green Brain Project. It has also been confirmed that the apian sense of smell can be used to detect explosive devices, such as TNT, or drugs (including heroin, cocaine, amphetamines and cannabis). This inconspicuous little insect can revolutionize the world of science and contribute to the solution of many scientific problems as a versatile model.

Beyond collective intelligence: Collective adaptation

We develop a conceptual framework for studying collective adaptation: the process of iterative co-adaptation of cognitive strategies, social environments, and problem structures. Going beyond searching for “intelligent” collectives, we integrate research from different disciplines to show how collective adaptation perspective can help explain why similar collectives can follow very different and sometimes counter-intuitive trajectories. We further discuss how this perspective explains why successful collectives appear to have a general collective intelligence factor, why collectives rarely optimize their behaviour for a single problem, why their behaviours can appear myopic, and why playful exploration of alternative social systems can be useful. We describe different approaches for the study of collective adaptation, including computational models inspired by evolution and statistical physics. The framework of collective adaptation enables the integration and formalization of knowledge about human collective phenomena and opens doors to a rigorous transdisciplinary pursuit of important outstanding questions about human sociality.

Collective Intelligence Under a Volatile Task Environment: a Behavioral Experiment Using Social Networks and Computer Simulations

Collective intelligence in our highly-connected world is a topic of interdisciplinary interest. Previous research has demonstrated that social network structures can affect collective intelligence, but the potential network impact is unknown when the task environment is volatile (i.e., optimal behavioral options can change over time), a common situation in modern societies. Here, we report a laboratory experiment in which a total of 250 participants performed a “restless” two-armed bandit task either alone, or collectively in a centralized or decentralized network. Although both network conditions outperformed the solo condition, no sizable performance difference was detected between the centralized and decentralized networks. To understand the absence of network effects, we analyzed participants’ behavior parametrically using an individual choice model. We then conducted exhaustive agent-based simulations to examine how different choice strategies may underlie collective performance in centralized or decentralized networks under volatile or stationary task environments. We found that, compared to the stationary environment, the difference in network structure had a much weaker impact on collective performance under the volatile environment across broad parametric variations. These results suggest that structural impacts of networks on collective intelligence may be constrained by the degree of environmental volatility.

Computational Thinking and Educational Technology: A Scoping Review of the Literature

Interest in computational thinking (CT) in the scientific community has increased significantly in the last 4 years, as evidenced by the numerous systematic reviews carried out. However, there is a lack of reviews that update the emerging conceptualization of CT and which also examine the roles of the school curriculum and teachers in the face of CT. A systematic literature review (SLR) consists of a collection of research conducted according to previous criteria with the aim of answering research questions with validity and quality. For this reason, the PRISMA-ScR statement was followed. Articles published in scientific journals, from Scopus and WoS, between January 2018 and August 2021 were included, in the English or Spanish language. The initial search resulted in 492 articles, to which the inclusion-exclusion criteria were applied. The final sample of texts for the present systematic review was n = 145. The texts were analyzed from three perspectives: conceptual, documentary and pedagogical. Thus, a renewal of previous literature reviews was carried out, updating the situation with research from recent years and new data, obtained to contribute to the collective intelligence on methodological strategies (80% of the sample was divided into “plugged” and “unplugged”); educational (more than 50% studied CT evaluation); and resources, including a collection of more than 119 educational resources.

Digital Organizations

Digitalization is changing business and organizations. In the organizational context, the potential that can be realized through digitalization is manifest, namely the expenditure reduction and higher innovative power. However, it is not enough to integrate digitalization; a change of culture and behavior is necessary. To this extent, social media relations have the potential to create communication capabilities that can be turned into useful resources, which in turn will result in more significant competitive advantage and performance. Taking advantage of social tools within social business contexts requires an exercise in how to demonstrate usefulness regarding the creation, access, and sharing of content securely. To this end, this chapter will provide a comprehensive view of a new context of labor (i.e., social business supported by mobile IST-m_CSDIT2) to improve the organizational well-being through the collective intelligence and agility dimensions.

Mapping the Mappers

Volunteered geographic information (VGI) platforms generate crowdsourced layers where a vast amount of shared and shareable geo-information is available. Monitoring the informative reliability of these sources is an important task, and the main VGI project, OpenStreetMap is a good testing ground to investigate how the collective intelligence made of users' networks creates public knowledge. OpenStreetMap (OSM) can be defined as a language of representation of real geographical entities shared as web maps. Mappers often work in solitude, but they stick to and strictly respect the rules given by their community. The aim is to create a geographical database used by anyone for any purpose. The chapter explores the following questions: How many contributors are there? Where are they and what do they collect? What are the interactions between them? The chapter illustrates what can be read from the OSM data, the available tools, and what could help researchers to understand this community.