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Swarm controlled emergence is proposed as an approach to control emergent effects in (artificial) swarms. The method involves the introduction of specific control agents into the swarm systems. Control agents behave similar to the normal agents and do not directly influence the behavior of the normal agents. The specific design of the control agents depends on the particular swarm system considered. The aim of this paper is to apply the method to ant clustering. Ant clustering, as an emergent effect, can be observed in nature and has inspired the design of several technical systems, e.g. moving robots, and clustering algorithms.

Different types of control agents for that ant clustering model are designed by introducing slight changes to the behavioural rules of the normal agents. The clustering behaviour of the resulting swarms is investigated by extensive simulation studies.

It is shown that complex behavior can emerge in systems with two types of agents (normal agents and control agents). For a particular behavior of the control agents, an interesting swarm size dependent effect was found. The behaviour prevents clustering when the number of control agents is large, but leads to stronger clustering when the number of control agents is relatively small.

Although swarm controlled emergence is a general approach, in the experiments of this paper the authors concentrate mainly on ant clustering. It remains for future research to investigate the application of the method in other swarm systems. Swarm controlled emergence might be applied to control emergent effects in computing systems that consist of many autonomous components which make decentralized decisions based on local information.

The particular finding, that certain behaviours of control agents can lead to stronger clustering, can help to design improved clustering algorithms by using heterogeneous swarms of agents.

In general, the control of (unwanted) emergent effects in artificial systems is an important problem. However, to date not much research has been done on this topic. This paper proposes a new approach and opens a different research direction towards future control principles for self‐organized systems that consist of a large number of autonomous components.

The purpose of this paper is to present an approach for the decentralization of swarm intelligence algorithms that run on computing systems with autonomous components that are connected by a network. The approach is applied to a particle swarm optimization (PSO) algorithm with multiple sub‐swarms. PSO is a nature inspired metaheuristic where a swarm of particles searches for an optimum of a function. A multiple sub‐swarms PSO can be used for example in applications where more than one optimum has to be found.

In the studied scenario the particles of the PSO algorithm correspond to data packets that are sent through the network of the computing system. Each data packet contains among other information the position of the corresponding particle in the search space and its sub‐swarm number. In the proposed decentralized PSO algorithm the application specific tasks, i.e. the function evaluations, are done by the autonomous components of the system. The more general tasks, like the dynamic clustering of data packets, are done by the routers of the network.

Simulation experiments show that the decentralized PSO algorithm can successfully find a set of minimum values for the used test functions. It was also shown that the PSO algorithm works well for different type of networks, like scale‐free network and ring like networks.

The proposed decentralization approach is interesting for the design of optimization algorithms that can run on computing systems that use principles of self‐organization and have no central control.

As we approach the millennium, we find ourselves in a world that places ever greater weight and significance on the outcome of polls, surveys, and market research. The advent of modern polling began with the use of scientific sampling in the mid‐1930s and has progressed vastly beyond the initial techniques and purposes of the early practitioners such as George Gallup, Elmo Roper, and Archibald Crossley. In today's environment, the computer is an integral part of most commercial survey work, as are the efforts by academic and nonprofit enterprises. It should be noted that the distinction between the use of the words “poll” and “survey” is somewhat arbitrary, with the mass media seeming to prefer “polling,” and with academia selecting “survey research.” However, searching online systems will yield differing results, hence this author's inclusion of both terms in the title of this article.

This paper aims at presenting a cyclical evaluation model of information security (IS) maturity. The lack of a security evaluation method might expose organizations to several risky situations.

This model was developed through the definition of a set of steps to be followed to obtain periodical evaluation of maturity and continuous improvement of controls.

This model, based on controls present in ISO/IEC 27002, provides a means to measure the current situation of IS management through the use of a maturity model and provides a subsidy to take appropriate and feasible improvement actions, based on risks. A case study is performed, and the results indicate that the method is efficient for evaluating the current state of IS, to support IS management, risks identification and business and internal control processes.

It is possible that modifications to the process may be needed where there is less understanding of security requirements, such as in a less mature organization.

This paper presents a generic model applicable to all kinds of organizations. The main contribution of this paper is the use of a maturity scale allied to the cyclical process of evaluation, providing the generation of immediate indicators for the management of IS.

The purpose of this paper is to explore the relationship between beliefs and economic policy in the context of gasoline prices following Hurricane Katrina.

The paper applies three contributions – by North, Caplan and Higgs – to the question of gasoline pricing policy; and surveys public opinion regarding interference with prices.

The paper identifies evidence of “anti‐market bias” in polling data, press releases, and legislation, and argues that the uncertainty emanating from statutes restricting “price gouging” may reduce investment in the provision of “necessary goods and services” after natural disasters.

The paper is of value in offering evidence of anti‐market and anti‐foreign bias among what might be called political first responders to Hurricane Katrina, and posits the view that interference with prices compounded the shortages facing the Gulf coast or any other disaster‐stricken area.

This meta-analysis reviews and summarizes the results of 34 studies to investigate the relationship between overconfidence and financial decision-making.

A correlation meta-analysis was conducted with three moderators of the relationship between overconfidence and financial decision-making examined: the type of overconfidence construct, the type of overconfidence measuring method and the type of financial decision-making.

It was found that the effect of overconfidence on financial decision-making was significant, but the magnitude of this effect was low. Additionally, indirect measures of overconfidence showed to have stronger effect than direct measures, and the overconfidence was mostly related to investment, followed by trading and innovativeness.

This was the first attempt to meta-analytically integrate results concerning the relationship between overconfidence and financial decision-making. Although overconfidence is described as a keystone for understanding financial decision-making, it was shown that it has rather limited effect on individuals' financial decisions. The findings suggest that indirect measures increase the overall effect and may cause the overvaluation of overconfidence in literature. The results call for more rigorous and consistent conceptualization of overconfidence in behavioral research.

This chapter introduces the concept of cryptocurrencies such as bitcoin, ether, or litecoin. The chapter describes the history of cryptocurrency, blockchain technology, and the quest for secure digital money, followed by a discussion of cryptocurrency as a phenomenon. Next, it discusses individual cryptocurrencies, including an overview of bitcoin and relevant subgroups, such as so-called forks or privacy coins. It also explains developments such as stablecoins or central bank digital currencies, which are potentially much more in line with bitcoin’s original idea of digital cash. Overall, the chapter provides a basic understanding of cryptocurrencies, their defining characteristics, challenges, and markets.

There is growing evidence that managers perceive the general environment inaccurately, but very few studies have looked at the accuracy of specific strategic issue probability estimates, and at whether or not managers are aware of the accuracy or inaccuracy of their perceptions, something referred to as knowledge miscalibration. I explore perceptual inaccuracy and knowledge miscalibration in the form of overconfidence, in the context of demographic ageing, an issue currently affecting the tourism and hospitality industry. Using data from a survey of hotel managers, I find a high prevalence of perceptual error and evidence of a relatively large minority of respondents displaying knowledge overconfidence. Furthermore, I find a link between accurate environmental perceptions and strategic issue importance, suggesting that managers are better at accurately perceiving an issue when it is strategically important for their business. The same link does not exist with overconfidence, lending support to scholars arguing that overconfidence may be a trait, rather than being question-specific.