Common Heuristics Research Articles

Exploring Personalized Tornado Risk with a Sketch Map Activity

Our study sought to understand how individuals think about their home location and county, including their geospatial awareness, and how they conceive of risk from a tornado geospatially. We conducted interviews and a sketch mapping activity with sixty-five individuals from Mississippi and Alabama. Our results indicated that participants could name and correctly locate only a few neighboring counties and that there was a difference of 4.7 miles on average between their perceived home location and their actual address. Participants labeled on average 9.1 features. Some maps displayed common heuristics or biases, but none was present in a majority of the sketch maps. Regarding geospatial risk personalization, participants’ drawings indicated that many inferred a tornado approaching from the southwest, and that they would take protective action when a tornado was within an average of fifteen miles of their home. Human landmarks and point locations were the features most frequently mentioned that would let participants know a tornado was coming close and participants estimated these features were on average 9.7 miles. Both distances showed a great deal of variation. The results have implications not only for tornado warning communication but also for understanding how individuals might think about a “close call.”

An approximation of populations on a habitat with large carrying capacity

We consider stochastic dynamics of a population which starts from a small colony on a habitat with large but limited carrying capacity. A common heuristics suggests that such population grows initially as a Galton–Watson branching process and then its size follows an almost deterministic path until reaching its maximum, sustainable by the habitat. In this paper we put forward an alternative and, in fact, more accurate approximation which suggests that the population size behaves as a special nonlinear transformation of the Galton–Watson process from the very beginning.

A Compendium of Common Heuristics, Misconceptions, and Biased Reasoning used in Statistical Thinking

A Compendium of Common Heuristics, Misconceptions, and Biased Reasoning used in Statistical Thinking

An Attention-Based Method for the Minimum Vertex Cover Problem on Complex Networks

Solving combinatorial problems on complex networks represents a primary issue which, on a large scale, requires the use of heuristics and approximate algorithms. Recently, neural methods have been proposed in this context to find feasible solutions for relevant computational problems over graphs. However, such methods have some drawbacks: (1) they use the same neural architecture for different combinatorial problems without introducing customizations that reflects the specificity of each problem; (2) they only use a nodes local information to compute the solution; (3) they do not take advantage of common heuristics or exact algorithms. Following this interest, in this research we address these three main points by designing a customized attention-based mechanism that uses both local and global information from the adjacency matrix to find approximate solutions for the Minimum Vertex Cover Problem. We evaluate our proposal with respect to a fast two-factor approximation algorithm and a widely adopted state-of-the-art heuristic both on synthetically generated instances and on benchmark graphs with different scales. Experimental results demonstrate that, on the one hand, the proposed methodology is able to outperform both the two-factor approximation algorithm and the heuristic on the test datasets, scaling even better than the heuristic with harder instances and, on the other hand, is able to provide a representation of the nodes which reflects the combinatorial structure of the problem.

Heuristics used for evaluating the usability of mobile health applications: A systematic literature review.

Mobile health applications hold immense potential for enhancing health outcomes. Usability is one of the main factors for the adoption and use of mobile health applications. However, despite the growing importance of mHealth applications, clear standards for their evaluation remain elusive. The present study aimed to determine heuristics for the usability evaluation of health-related applications. We systematically searched multiple databases for relevant papers published between January 2008 and April 2021. Articles were reviewed, and data were extracted and categorized from those meeting inclusion criteria by two authors independently. Heuristics were identified based on statements, words, and concepts expressed in the studies. These heuristics were first mapped to Nielsen's heuristics based on their differences or similarities. The remaining heuristics that were very important for mobile applications were categorized into new heuristics. Seventeen studies met the eligibility criteria. Seventy-nine heuristics were extracted from the papers. After combining the items with the same concepts and removing irrelevant items based on the exclusion criteria, 20 heuristics remained. Common heuristics such as "Visibility of system status" and "Flexibility and efficiency of use" were categorized into 10 previously established heuristics and new heuristics like "Navigation" and "User engagement" were recognized as new ones. In our study, we have meticulously identified 20 heuristics that hold promise for evaluating and designing mHealth applications. These heuristics can be used by the researchers for the development of robust tools for heuristic evaluation. These tools, when adapted or tailored for health domain applications, have the potential to significantly enhance the quality of mHealth applications. Ultimately, this improvement in quality translates to enhanced patient safety. (10.17605/OSF.IO/PZJ7H).

Bounds and Heuristics for Multiproduct Pricing

For a large class of demand models that allow for multiple consumer types, we present performance guarantees for simple nonpersonalized pricing heuristics relative to optimal personalized pricing. Our results demonstrate that in a general setting, the effectiveness of pricing along a positive vector depends on how the price vector aligns with optimal personalized price vectors. We propose two positive direction vectors: the “economic” and “robust” directions. The economic direction is a convex combination of the optimal personalized price vectors and aims to do well on average. The robust direction offers the best worst-case performance guarantee. By judiciously selecting pricing directions, our results also provide performance guarantees of simple pricing strategies relative to more sophisticated pricing strategies. In particular, we provide performance guarantees for nonpersonalized, optimal linear pricing relative to optimal nonlinear, personalized pricing. Our research also examines the performance of common heuristics for bundle pricing relative to optimal, personalized, bundle-size pricing. Our experiments show that performance often improves when consumer types are clustered and each cluster is offered a price direction. We compared the performance of the k-means clustering heuristic and the farthest point first clustering heuristic. Our findings indicated that k-means clustering has significantly superior performance on average. This suggests that businesses could potentially benefit from implementing k-means clustering in their pricing strategies. In conclusion, our study offers valuable insights and performance guarantees for various pricing strategies and their relative effectiveness. These findings could inform pricing decisions and potentially lead to improved outcomes for firms. This paper was accepted by Chung-Piaw Teo, optimization. Funding: This work was supported by Collaborative Research Funding (CRF) Hong Kong [Grant (CRF) C6032-21G], RGC Hong Kong [Grant 16502819], and Hong Kong University of Science and Technology. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.04173 .

Improving node connectivity by optimized dual tree-based effective node consolidation

As an effective way to defend against network failure, nodes or links in a network can be protected to ensure connectivity. The consolidation of critical nodes is a necessary strategy, especially when nodes are important infrastructure elements, such as in wireless environments. However, since the problem is NP-hard, even the optimal algorithm is of high computational complexity on small-scale graphs, which makes it difficult to handle large-scale networks in feasible time. Fortunately, we have found an effective pairwise node cut set (NCS)-tree search method to locate small NCSes. The critical nodes in these NCSes are then selected to construct the optimal dual tree, and to determine the near optimal set of consolidation nodes. Comparison results show that the serial version of the proposed algorithm achieves a remarkable acceleration ratio of more than 105 compared to the optimal algorithm, with only a minimal additional cost. In addition, the consolidation solution provides protection for over 99.9% of vulnerable node pairs in large-scale networks and can significantly outperform common heuristics. Moreover, the ready-to-parallelization algorithm has the potential to be further accelerated hundreds of times in full parallelization.

BalCon — resource balancing algorithm for VM consolidation

Cloud providers handle substantial number of requests to create and delete virtual machines (VMs) on a daily basis, where the unknown sequence of requests eventually leads to resource fragmentation. To mitigate this issue, periodic consolidation of VMs into fewer number of physical hosts is an important cost-saving procedure, closely related to the vector bin-packing problem. In this paper, we propose the BalCon algorithm for consolidation that steadily reduces the number of active hosts and keeps migration costs low. BalCon classifies the cluster’s state and selects one of three heuristics to balance resources for superior consolidation. To evaluate BalCon’s performance with respect to optimality, we introduce integer programming models. BalCon finds 99.7% of the optimal solutions for over 750 problem instances. This outstanding result was achieved due to the Force Step of our algorithm, which is the key improvement detail for common heuristics. We compare BalCon with a modified Sercon heuristic using Huawei and synthetic datasets with two resources for allocation.

A Deep Learning Game Theoretic Model for Defending Against Large Scale Smart Grid Attacks

Power grids that are interdependent with communication networks create more possible modes of failure (e.g., cyberattacks) as well as more complex propagation of failure through the coupled networks. To ensure robust defense of smart grids, it is important to model both attacker and defender as intelligent, a scenario that the framework of game theory provides methods to analyze. However, prior works in applying game theoretic models to smart grid security limit the problem space to a small number targets under threat due to the inability of state-of-the-art methods to scale to large networks. Our method scales to large networks by combining neural networks that use featurized action representations with an approximation of large combinatorial actions to generalize knowledge about the best targets to attack/defend across graphs of various topologies and sizes. Our model’s invariance to the size of the input graph allows us to transfer knowledge from games played on small graphs during training to large graphs during evaluation. Our experiments show that our method can learn Nash equilibrium strategies on small networks, and demonstrate low exploitability when generalized to large networks, especially compared to the common heuristics currently used to simulate attacks on large graphs.

Site Assessment and Layout Optimization for Rooftop Solar Energy Generation in Worldview-3 Imagery

With the growth of residential rooftop PV adoption in recent decades, the problem of effective layout design has become increasingly important in recent years. Although a number of automated methods have been introduced, these tend to rely on simplifying assumptions and heuristics to improve computational tractability. We demonstrate a fully automated layout design pipeline that attempts to solve a more general formulation with greater geometric flexibility that accounts for shading losses. Our approach generates rooftop areas from satellite imagery and uses MINLP optimization to select panel positions, azimuth angles and tilt angles on an individual basis rather than imposing any predefined layouts. Our results demonstrate that shading plays a critical role in automated rooftop PV optimization and significantly changes the resulting layouts. Additionally, they suggest that, although several common heuristics are often effective, they may not be universally suitable due to complications resulting from geometric restrictions and shading losses. Finally, we evaluate a few specific heuristics from the literature and propose a potential new rule of thumb that may help improve rooftop solar energy potential when shading effects are considered.

Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction

Data-driven synthesis planning with machine learning is a key step in the design and discovery of novel inorganic compounds with desirable properties. Inorganic materials synthesis is often guided by heuristics and chemists’ prior knowledge and experience, built upon experimental trial-and-error that can be both time and resource consuming. Recent developments in natural language processing have enabled large-scale text mining of scientific literature, providing open-source databases of synthesis information on realized compounds, material precursors, and reaction conditions (temperatures, times). We employ supervised classification machine learning (ML) models to distinguish between solid-state, sol–gel, and solution (hydrothermal, precipitation) synthesis routes based on specified reaction target material and/or precursor materials. We demonstrate regression ML models that are able to predict suitable temperatures and times for the crucial inorganic synthesis steps of calcination and sintering given the reaction target and precursor materials. We contrast this regression-based condition modeling with a conditional variational autoencoder neural network that can generate appropriate distributions for the synthesis conditions of interest. We evaluate model interpretability using the Shapley additive explanations approach to gain insight into factors influencing suitability of synthesis route and reaction conditions. We find that the aforementioned models are capable of learning subtle differences in target material composition, precursor compound identities, and choice of synthesis route that are present in the inorganic synthesis space. Moreover, they generalize well to unseen chemical entities, outperform common heuristics in the field, and show promise for predicting appropriate reaction routes and conditions for previously unsynthesized compounds of interest.

An Analysis of Traditional Noise Power Spectral Density Estimators Based on the Gaussian Stochastic Volatility Model

Many single- and multi-channel speech enhancement techniques, old and new, rely in one way or another on estimates of the noise power spectral density (PSD). For example, the classical Wiener filter requires that either the speech or noise PSD be estimated. Typically, the noise PSD is estimated, as it is often easier to model and estimate than the speech. As a result, much attention has been paid to this important problem over the past couple of decades, with important scientific milestones being the minimum statistics (MS), the minima controlled recursive averaging (IMCRA), and the minimum mean squared (MMSE) estimators. Despite leading to major progress, these estimators are rather ad hoc, making them difficult to tune and improve in a systematic manner. In this article, we analyse some of the common heuristics employed in such noise PSD estimators to put them on firmer mathematical ground. More specifically, we use the Gaussian stochastic volatility model and show that the MMSE noise PSD estimator can be interpreted as a special case thereof. Moreover, we analyze the related problem of speech presence probability (SPP) estimation and show that the SPP estimation performed in the MMSE noise PSD estimator can be interpreted as an SNR estimator in the context of the Gaussian stochastic volatility model.

Clinical decision making in physical therapy – Exploring the ‘heuristic’ in clinical practice

Clinical decision-making (CDM) plays an integral role in the work of a physical therapist and has ramifications for patient outcomes and experience. Rational decision-making – acting in a manner that helps us achieve our goals – is influenced by cognitive, emotional, and social variables. The dual process theory helps us understand how clinicians make what they perceive to be rational decisions. Within dual process is the use of cognitive decisional shortcuts, commonly referred to as ‘heuristics,’ which are either developed through experience or the use of fast and frugal trees (FFT). The use of heuristics in physical therapy practice has yet to be explored. This paper aims to describe this subset of physical therapy decision-making and to identify the typical cognitive biases - the error in heuristic-driven decision making - inherent in this style of reasoning. Common heuristics and their related biases are described and illustrated with vignettes, including the anchoring, availability, confirmatory, and representative heuristics. The authors conclude by proposing interventions to optimize physical therapists' awareness of their use of heuristics in clinical decision-making and to identify and minimize the influence of potential bias.

Evolving action pre-selection parameters for MCTS in real-time strategy games

Real-Time Strategy (RTS) games are well-known for their substantially large combinatorial decision and state spaces, responsible for creating significant challenges for search and machine learning techniques. Exploiting domain knowledge to assist in navigating the expansive decision and state spaces could facilitate the emergence of competitive RTS game-playing agents. Usually, domain knowledge can take the form of expert traces or expert-authored scripts. A script encodes a strategy conceived by a human expert and can be used to steer a search algorithm, such as Monte Carlo Tree Search (MCTS), towards high-value states. However, a script is coarse by nature, meaning that it could be subject to exploitation and poor low-level tactical performance. We propose to perceive scripts as a collection of heuristics that can be parameterized and combined to form a wide array of strategies. The parameterized heuristics mold and filter the decision space in favor of a strategy expressed in terms of parameters. The proposed agent, ParaMCTS, implements several common heuristics and uses NaïveMCTS to search the downsized decision space; however, it requires a preceding manual parameterization step. A genetic algorithm is proposed for use in an optimization phase that aims to replace manual tuning and find an optimal set of parameters for use by EvoPMCTS, the evolutionary counterpart of ParaMCTS. Experimentation results using the μRTS testbed show that EvoPMCTS outperforms several state-of-the-art agents across multiple maps of distinct layouts.

Can We Do Better Next Time? Italians' Response to the COVID-19 Emergency through a Heuristics and Biases Lens.

During the outbreak of COVID-19 in Italy, people often failed to adopt behaviors that could have stopped, or at least slowed down, the spread of this deadly disease. We offer cognitive explanations for these decisions, based on some of the most common heuristics and biases that are known to influence human judgment and decision-making, especially under conditions of high uncertainty. Our analysis concludes with the following recommendations: policymakers can and should take advantage of this established science, in order to communicate more effectively and increase the likelihood that people choose responsible actions in a public health crisis.

Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising

One of the central challenges in online advertising is attribution, namely, assessing the contribution of individual advertiser actions such as emails, display ads, and search ads to eventual conversion. Several heuristics are used for attribution in practice; however, most do not have any formal justification. The main contribution in this work is to propose an axiomatic framework for attribution in online advertising. We show that the most common heuristics can be cast under the framework and illustrate how these may fail. We propose a novel attribution metric, which we refer to as counterfactual adjusted Shapley value (CASV), which inherits the desirable properties of the traditional Shapley value while overcoming its shortcomings in the online advertising context. We also propose a Markovian model for the user journey through the conversion funnel, in which ad actions may have disparate impacts at different stages. We use the Markovian model to compare our metric with commonly used metrics. Furthermore, under the Markovian model, we establish that the CASV metric coincides with an adjusted “unique-uniform” attribution scheme. This scheme is efficiently implementable and can be interpreted as a correction to the commonly used uniform attribution scheme. We supplement our theoretical developments with numerical experiments using a real-world large-scale data set. This paper was accepted by David Simchi-Levi, revenue management and market analytics.

Testing Causal Theories with Learned Proxies

Social scientists commonly use computational models to estimate proxies of unobserved concepts, then incorporate these proxies into subsequent tests of their theories. The consequences of this practice, which occurs in over two-thirds of recent computational work in political science, are underappreciated. Imperfect proxies can reflect noise and contamination from other concepts, producing biased point estimates and standard errors. We demonstrate how analysts can use causal diagrams to articulate theoretical concepts and their relationships to estimated proxies, then apply straightforward rules to assess which conclusions are rigorously supportable. We formalize and extend common heuristics for “signing the bias”—a technique for reasoning about unobserved confounding—to scenarios with imperfect proxies. Using these tools, we demonstrate how, in often-encountered research settings, proxy-based analyses allow for valid tests for the existence and direction of theorized effects. We conclude with best-practice recommendations for the rapidly growing literature using learned proxies to test causal theories.

SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems

We study combinatorial problems with real world applications such as machine scheduling, routing, and assignment. We propose a method that combines Reinforcement Learning (RL) and planning. This method can equally be applied to both the offline, as well as online, variants of the combinatorial problem, in which the problem components (e.g., jobs in scheduling problems) are not known in advance, but rather arrive during the decision-making process. Our solution is quite generic, scalable, and leverages distributional knowledge of the problem parameters. We frame the solution process as an MDP, and take a Deep Q-Learning approach wherein states are represented as graphs, thereby allowing our trained policies to deal with arbitrary changes in a principled manner. Though learned policies work well in expectation, small deviations can have substantial negative effects in combinatorial settings. We mitigate these drawbacks by employing our graph-convolutional policies as non-optimal heuristics in a compatible search algorithm, Monte Carlo Tree Search, to significantly improve overall performance. We demonstrate our method on two problems: Machine Scheduling and Capacitated Vehicle Routing. We show that our method outperforms custom-tailored mathematical solvers, state of the art learning-based algorithms, and common heuristics, both in computation time and performance.

Product Bundle Recommendation and Pricing: How to Make It Work?

Product bundling is a common marketing strategy for cross-selling in multiproduct firms. Motivated by settings in online product recommendation, we propose a new approach, dubbed recommendation and pricing (BRP), to enhance the performance of recommendation system. BRP keeps all the separately priced products in the recommended set, and adds a subset of products as a new with a discounted price to customers. This approach extends pure bundling (PB), where all the products are sold in a single with a discounted price to customers. Although PB can be more profitable than component pricing (CP) where products are priced and sold separately, it can be inferior to in the presence of high marginal cost. We show that such a simple CP + one bundle scheme can be more profitable than both PB and CP, and is near optimal in many environments. BRP improves by extracting the deadweight loss, but retains the profitability of when some products have relatively high marginal costs. However, finding the optimal BRP solution is often intractable. We develop a new approximation to this problem and use a Bayesian optimization algorithm to optimize the selection and pricing decisions. Extensive numerical results show that our algorithm outperforms other common heuristics. More importantly, by simply adding one more option to the common mechanism, our results show that BRP tends to significantly increase both the monopolist's profit and customers' utility as compared with and PB.

Data-Driven Heuristic Induction From Human Design Behavior

Abstract Through experience, designers develop guiding principles, or heuristics, to aid decision-making in familiar design domains. Generalized versions of common design heuristics have been identified across multiple domains and applied by novices to design problems. Previous work leveraged a sample of these common heuristics to assist in an agent-based design process, which typically lacks heuristics. These predefined heuristics were translated into sequences of specifically applied design changes that followed the theme of the heuristic. To overcome the upfront burden, need for human interpretation, and lack of generality of this manual process, this paper presents a methodology that induces frequent heuristic sequences from an existing timeseries design change dataset. Individual induced sequences are then algorithmically grouped based on similarity to form groups that each represent a shared general heuristic. The heuristic induction methodology is applied to data from two human design studies in different design domains. The first dataset, collected from a truss design task, finds a highly similar set of general heuristics used by human designers to that which was hand-selected for the previous computational agent study. The second dataset, collected from a cooling system design problem, demonstrates further applicability and generality of the heuristic induction process. Through this heuristic induction technique, designers working in a specified domain can learn from others’ prior problem-solving strategies and use these strategies in their own future design problems.