•This paper presents a framework of search that connects the search dimension of “where to search: local and distant” with “how to search: experiential and cognitive.”•Combining the search space and ...search heuristic dimensions, we suggest a framework comprising four search paths: situated, analogical, sophisticated, and scientific.•Each search path is detailed by illustrations from 18 open innovation projects carried out by an innovation intermediary and different knowledge-seeking clients.•We discuss how the mechanisms of problem framing and boundary spanning assist each search path.
Search for external knowledge is vital for firms’ innovative activities. To understand search, we propose two knowledge search dimensions: search space (local or distant) and search heuristics (experiential or cognitive). Combining these two dimensions, we distinguish four search paths – situated paths, analogical paths, sophisticated paths, and scientific paths – which respond to recent calls to move beyond “where to search” and to investigate the connection with “how to search.” Also, we highlight how the mechanisms of problem framing and boundary spanning operate within each search path to identify solutions to technology problems. We report on a study of 18 open innovation projects that used an innovation intermediary, and outline the characteristics of each search path. Exploration of these search paths enriches previous studies of search in open innovation by providing a comprehensive, but structured, framework that explains search, its underlying mechanisms, and potential outcomes.
Heuristic search guides the exploration of states via heuristic functions h estimating remaining cost. Symbolic search instead replaces the exploration of individual states with that of state sets, ...compactly represented using binary decision diagrams (BDDs). In cost-optimal planning, heuristic explicit search performs best overall, but symbolic search performs best in many individual domains, so both approaches together constitute the state of the art. Yet combinations of the two have so far not been an unqualified success, because (i) h must be applicable to sets of states rather than individual ones, and (ii) the different state partitioning induced by h may be detrimental for BDD size. Many competitive heuristic functions in planning do not qualify for (i), and it has been shown that even extremely informed heuristics can deteriorate search performance due to (ii).
Here we show how to achieve (i) for a state-of-the-art family of heuristic functions, namely potential heuristics. These assign a fixed potential value to each state-variable/value pair, ensuring by LP constraints that the sum over these values, for any state, yields an admissible and consistent heuristic function. Our key observation is that we can express potential heuristics through fixed potential values for operators instead, capturing the change of heuristic value induced by each operator. These reformulated heuristics satisfy (i) because we can express the heuristic value change as part of the BDD transition relation in symbolic search steps. We run exhaustive experiments on IPC benchmarks, evaluating several different instantiations of potential heuristics in forward, backward, and bi-directional symbolic search. Our operator-potential heuristics turn out to be highly beneficial, in particular they hardly ever suffer from (ii). Our best configurations soundly beat previous optimal symbolic planning algorithms, bringing them on par with the state of the art in optimal heuristic explicit search planning in overall performance.
Risk, uncertainty, and heuristics Mousavi, Shabnam; Gigerenzer, Gerd
Journal of business research,
08/2014, Volume:
67, Issue:
8
Journal Article
Peer reviewed
Open access
Nearly a century ago, Frank Knight famously distinguished between risk and uncertainty with respect to the nature of decisions made in a business enterprise. He associated generating economic profit ...with making entrepreneurial decisions in the face of fundamental uncertainties. This uncertainty is complex because it cannot be reliably hedged unless it is reducible to risk. In making sense of uncertainty, the mathematics of probability that is used for risk calculations may lose relevance. Fast-and-frugal heuristics, on the other hand, provide robust strategies that can perform well under uncertainty. The present paper describes the structure and nature of such heuristics and provides conditions under which each class of heuristics performs successfully. Dealing with uncertainty requires knowledge but not necessarily an exhaustive use of information. In many business situations, effective heuristic decision-making deliberately ignores information and hence uses fewer resources. In an uncertain world, less often proves to be more.
Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An ...underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.
This paper presents an alternative method for cloud task scheduling problem which aims to minimize makespan that required to schedule a number of tasks on different Virtual Machines (VMs). The ...proposed method is based on the improvement of the Moth Search Algorithm (MSA) using the Differential Evolution (DE). The MSA simulates the behavior of moths to fly towards the source of light in nature through using two concepts, the phototaxis and Levy flights that represent the exploration and exploitation ability respectively. However, the exploitation ability is still needed to be improved, therefore, the DE can be used as local search method. In order to evaluate the performance of the proposed MSDE algorithm, a set of three experimental series are performed. The first experiment aims to compare the traditional MSA and the proposed algorithm to solve a set of twenty global optimization problems. Meanwhile, in second and third experimental series the performance of the proposed algorithm to solve the cloud task scheduling problem is compared against other heuristic and meta-heuristic algorithms for synthetical and real trace data, respectively. The results of the two experimental series show that the proposed algorithm outperformed other algorithms according to the performance measures.
•An alternative method for cloud task scheduling problem.•The proposed method is a modified moth search algorithm using Differential Evolution.•The Differential Evolution is used as local search method to enhance moth search algorithm.•The proposed method is evaluated twenty optimization functions.•It is used to solve the cloud task scheduling using synthetic and real trace data.
The relevance of several cognitive heuristics and related biases for rational choice perspectives on crime, and for perceptions of sanction risk, were investigated. We present findings from a series ...of randomized experiments, embedded in two nationwide surveys of American adults (18 and older) in 2015 (N = 1,004 and 623). The results reveal that offender estimates of detection risk are less probabilistically precise and more situationally variable than under prevailing criminological perspectives, most notably, rational choice and Bayesian learning theories. This, in turn, allows various decision‐making heuristics—such as anchoring and availability—to influence and potentially bias the perceptual updating process.
Human communication is increasingly intermixed with language generated by AI. Across chat, email, and social media, AI systems suggest words, complete sentences, or produce entire conversations. ...AI-generated language is often not identified as such but presented as language written by humans, raising concerns about novel forms of deception and manipulation. Here, we study how humans discern whether verbal self-presentations, one of the most personal and consequential forms of language, were generated by AI. In six experiments, participants (N = 4,600) were unable to detect self-presentations generated by state-of-the-art AI language models in professional, hospitality, and dating contexts. A computational analysis of language features shows that human judgments of AI-generated language are hindered by intuitive but flawed heuristics such as associating first-person pronouns, use of contractions, or family topics with human-written language. We experimentally demonstrate that these heuristics make human judgment of AI-generated language predictable and manipulable, allowing AI systems to produce text perceived as "more human than human." We discuss solutions, such as AI accents, to reduce the deceptive potential of language generated by AI, limiting the subversion of human intuition.
A global field experiment with Seeking Alpha shows that textual complexity affects investor attention to news and market outcomes. Investors were randomly assigned different titles for the same news ...article. Holding the article fixed, a one-standard-deviation increase in complexity leads to 6.1% fewer views. Complexity is more off-putting for less-sophisticated investors, when attention is more limited, and when the news is likely less important. Exploiting an arbitrary rule for breaking ties between tested titles, I find that title complexity affects markets—lowering announcement turnover and volatility.
•Textual complexity reduces investor attention to news•Less-sophisticated investors are more negatively affected by complexity•Complexity matters more when investors' attention is more limited•Complexity aversion declines when news is likely more interesting•Complexity reduces announcement turnover and volatility
In this paper, a new production, allocation, location, inventory holding, distribution, and flow problems for a new sustainable-resilient health care network related to the COVID-19 pandemic under ...uncertainty is developed that also integrated sustainability aspects and resiliency concepts. Then, a multi-period, multi-product, multi-objective, and multi-echelon mixed-integer linear programming model for the current network is formulated and designed. Formulating a new MILP model to design a sustainable-resilience healthcare network during the COVID-19 pandemic and developing three hybrid meta-heuristic algorithms are among the most important contributions of this research. In order to estimate the values of the required demand for medicines, the simulation approach is employed. To cope with uncertain parameters, stochastic chance-constraint programming is proposed. This paper also proposed three meta-heuristic methods including Multi-Objective Teaching–learning-based optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to find Pareto solutions. Since heuristic approaches are sensitive to input parameters, the Taguchi approach is suggested to control and tune the parameters. A comparison is performed by using eight assessment metrics to validate the quality of the obtained Pareto frontier by the heuristic methods on the experiment problems. To validate the current model, a set of sensitivity analysis on important parameters and a real case study in the United States are provided. Based on the empirical experimental results, computational time and eight assessment metrics proposed methodology seems to work well for the considered problems. The results show that by raising the transportation costs, the total cost and the environmental impacts of sustainability increased steadily and the trend of the social responsibility of staff rose gradually between − 20 and 0%, but, dropped suddenly from 0 to + 20%. Also in terms of the on-resiliency of the proposed network, the trends climbed slightly and steadily. Applications of this paper can be useful for hospitals, pharmacies, distributors, medicine manufacturers and the Ministry of Health.
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