A distinctive feature of software‐defined networking (SDN) is a logically centralized control plane realized using multiple physical controllers. The placement of the controllers, the so‐called ...controller placement problem (CPP), is a crucial design issue. It influences network performance parameters such as latency, flow setup time, network availability, load balance of the controllers, and energy consumption. In this article, we illustrate the formulation of these CPP objectives. We categorize the CPP design solutions as either static or adaptive. In adaptive CPP, the solutions proposed dynamically adapt to the number of controllers required and the switch to controller mapping to varying network traffic. We further differentiate adaptive CPP as wired or wireless. The optimization strategies adopted by the papers are analyzed and grouped into five categories: exact, heuristic, meta‐heuristic, clustering, and game theory. The merits and demerits of each approach are discussed. In conclusion, we outline the research challenges worth investigating.
This paper aims to tackle the Patient Admission Scheduling Problem (PASP) using the Discrete Flower Pollination Algorithm (DFPA), a new, meta-heuristic optimization method based on plant pollination. ...PASP is one of the most important problems in the field of health care. It is a highly constrained and combinatorial optimization problem of assigning patients to medical resources in a hospital, subject to predefined constraints, while maximizing patient comfort. While the flower pollination algorithm was designed for continuous optimization domains, a discretization of the algorithm has been carried out for application to the PASP. Various neighborhood structures have been employed to enhance the method, and to explore more solutions in the search space. The proposed method has been tested on six instances of benchmark datasets for comparison against another algorithm using the same dataset. The prospective method is shown to be very efficient in solving any scheduling problem.
•A discrete flower pollination algorithm is proposed for PASP.•A discretization procedure is invoked to address such kind of problem.•3.Three neighborhood structures have been employed to enhance the method.•The proposed method has been successful in gaining promising feasible solutions.
Results from four studies show that the reliance on affect as a heuristic of judgment and decision making is more pronounced under a
promotion focus than under a
prevention focus. Two different ...manifestations of this phenomenon were observed. Studies 1–3 show that different types of affective inputs are weighted more heavily under promotion than under prevention in person-impression formation, product evaluations, and social recommendations. Study 4 additionally shows that valuations performed under promotion are more scope-insensitive—a characteristic of affect-based valuations—than valuations performed under prevention. The greater reliance on affect as a heuristic under promotion seems to arise because promotion-focused individuals tend to find affective inputs more diagnostic, not because promotion increases the reliance on peripheral information
per se.
It is broadly assumed that political elites (e.g. party leaders) regularly rely on heuristics in their judgments or decision-making. In this article, I aim to bring together and discuss the scattered ...literature on this topic. To address the current conceptual unclarity, I discuss two traditions on heuristics: (1) the heuristics and biases (H&B) tradition pioneered by Kahneman and Tversky and (2) the fast and frugal heuristics (F&F) tradition pioneered by Gigerenzer et al. I propose to concentrate on two well-defined heuristics from the H&B tradition—availability and representativeness—to empirically assess when political elites rely on heuristics and thereby understand better their judgments and decisions. My review of existing studies supports the notion that political elites use the availability heuristic and possibly the representativeness one for making complex decisions under uncertainty. It also reveals that besides this, we still know relatively little about when political elites use which heuristic and with what effect(s). Therefore, I end by proposing an agenda for future research.
Research summary
Enterprises in low‐resource contexts often rely on bricolage (i.e., making do by applying resources at hand to new problems). However, bricolage has traditionally been regarded as a ...way to temporarily get by, potentially constraining growth if continued over time. This has been explained by factors such as limited development of learning competencies. Surprisingly, we encountered a social organization appearing to use bricolage to scale extensively into a variety of locations. This puzzling observation prompted our research question: Can bricolage be scaled, and if so, how and why? We embarked on a process study of this organization, leading to a novel conceptual model of scaling bricolage: as a low‐cost replication process of heuristics, enabling fit with a diversity of local environments, as well as cross‐unit learning.
Managerial summary
How do organizations emerge, survive, and scale in resource‐scarce environments? Traditional scaling models tend to rely on considerable financial resources and companies often struggle to adjust to diverse contexts. In contrast, we identified and studied an organization in Sub‐Saharan Africa that we argue used simple rules to scale bricolage—making the best out of what is at hand—successfully in diverse low‐resource contexts. Our paper provides a novel conceptual model of scaling bricolage: a low‐cost replication process of heuristics, enabling fit with a diversity of local environments, as well as cross‐unit innovation and learning.
Research shows that evidence-based algorithms more accurately predict the future than do human forecasters. Yet when forecasters are deciding whether to use a human forecaster or a statistical ...algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion, is costly, and it is important to understand its causes. We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake. In 5 studies, participants either saw an algorithm make forecasts, a human make forecasts, both, or neither. They then decided whether to tie their incentives to the future predictions of the algorithm or the human. Participants who saw the algorithm perform were less confident in it, and less likely to choose it over an inferior human forecaster. This was true even among those who saw the algorithm outperform the human.
Much of the literature on polarization and selective exposure presumes that the internet exacerbates the fragmentation of the media and the citizenry. Yet this ignores how the widespread use of ...social media changes news consumption. Social media provide readers a choice of stories from different sources that come recommended from politically heterogeneous individuals, in a context that emphasizes social value over partisan affiliation. Building on existing models of news selectivity to emphasize information utility, we hypothesize that social media’s distinctive feature, social endorsements, trigger several decision heuristics that suggest utility. In two experiments, we demonstrate that stronger social endorsements increase the probability that people select content and that their presence reduces partisan selective exposure to levels indistinguishable from chance.
Risks and benefits are negatively related in people’s minds. Finucane et al. causally demonstrated that increasing risks of a hazard leads people to judge its benefits as lower. Vice versa, ...increasing benefits leads people to judge its risks as lower (original: r = −.74 −0.92, −0.30). This finding is consistent with an affective explanation, and the negative relationship is often presented as evidence for an affect heuristic. In two well-powered studies, using a more stringent analytic strategy, we replicated the original finding. We observed a strong negative relationship between judgments of risks and benefits across three technologies, although we do find that there was no change in risks when highlighting low benefits. We note that risks seem to be more responsive to manipulation (as opposed to benefits) and find evidence that the negative relationship can depend on incidental mood. We provided materials, data sets, and analyses on https://osf.io/sufjn/?view_only=6f8f5dc6ff524149a4ed5c6de9296ae8.
Are humans intuitively altruistic, or does altruism require self-control? A theory of social heuristics, whereby intuitive responses favor typically successful behaviors, suggests that the answer may ...depend on who you are. In particular, evidence suggests that women are expected to behave altruistically, and are punished for failing to be altruistic, to a much greater extent than men. Thus, women (but not men) may internalize altruism as their intuitive response. Indeed, a meta-analysis of 13 new experiments and 9 experiments from other groups found that promoting intuition relative to deliberation increased giving in a Dictator Game among women, but not among men (Study 1, N = 4,366). Furthermore, this effect was shown to be moderated by explicit sex role identification (Study 2, N = 1,831): the more women described themselves using traditionally masculine attributes (e.g., dominance, independence) relative to traditionally feminine attributes (e.g., warmth, tenderness), the more deliberation reduced their altruism. Our findings shed light on the connection between gender and altruism, and highlight the importance of social heuristics in human prosociality.
Fast and frugal forecasting Goldstein, Daniel G.; Gigerenzer, Gerd
International journal of forecasting,
10/2009, Volume:
25, Issue:
4
Journal Article
Peer reviewed
Open access
Simple statistical forecasting rules, which are usually simplifications of classical models, have been shown to make better predictions than more complex rules, especially when the future values of a ...criterion are highly uncertain. In this article, we provide evidence that some of the fast and frugal heuristics that people use intuitively are able to make forecasts that are as good as or better than those of knowledge-intensive procedures. We draw from research on the adaptive toolbox and ecological rationality to demonstrate the power of using intuitive heuristics for forecasting in various domains including sport, business, and crime.
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