Modeling reproductive decisions with simple heuristics

The two main ideas of this chapter are ‘ecological rationality’ and ‘fast and frugal’ heuristics. Ecological rationality aims to understand how people can be effective decision-makers by using simple heuristics or ‘rules of thumb’ that fit well with the structure of their actual decision-making environment (hence ‘ecological’). It has three principles: first, the metaphor of the mind as an ‘adaptive toolbox’; second, the mind’s ‘evolved capacities’ are raw materials out of which heuristics are constructed; third, better decisions can be taken using less information—‘less can be more’. Fast-and-fugal heuristics are simple, task-specific strategies for making judgements and taking decisions; they are one of the tools of bounded rationality. Gerd Gigerenzer’s work is centre-stage. His view of intuition aligns with an evolutionary view of the mind as an adaptive toolbox. Gigerenzer criticizes ‘Linda-type’ problems because they overlook the cognitive skills that enable human beings to go automatically beyond the information given to navigate an ambiguous and uncertain world. The chapter looks at three types of ‘fast-and-frugals’, the recognition-, social- and reason-based heuristics, and considers their relevance to and applications in business decision-making.

This article reviews how simple heuristics – ‘rules of thumb’ – have guided human adaptation and the evolution of complex cultures. First, we argue that rules of thumb have been important catalysts for the evolution of human knowledge systems in the Holocene past. Through a variety of examples and case studies, we discuss how human cultures have used simple heuristics in domains as diverse as foraging, agriculture, social learning, moral and legal judgement and technological development. We emphasise how rules of thumb are convenient units for cultural transmission, and how they can facilitate efficient decision making by making use of recurrent environmental features. Second, we caution that as Anthropocene environments rapidly change, many traditional heuristic problem-solving strategies will face challenges due to cultural evolutionary mismatch. Old rules may not function in new environments, creating potential challenges for traditional (ecological) knowledge.

Animal foraging routes are analogous to the computationally demanding "traveling salesman problem" (TSP), where individuals must find the shortest path among several locations before returning to the start. Humans approximate solutions to TSPs using simple heuristics or "rules of thumb," but our knowledge of how other animals solve multidestination routing problems is incomplete. Most nonhuman primate species have shown limited ability to route plan. However, captive vervets were shown to solve a TSP for six sites. These results were consistent with either planning three steps ahead or a risk-avoidance strategy. I investigated how wild vervet monkeys (Chlorocebus pygerythrus) solved a path problem with six, equally rewarding food sites; where site arrangement allowed assessment of whether vervets found the shortest route and/or used paths consistent with one of three simple heuristics to navigate. Single vervets took the shortest possible path in fewer than half of the trials, usually in ways consistent with the most efficient heuristic (the convex hull). When in competition, vervets' paths were consistent with different, more efficient heuristics dependent on their dominance rank (a cluster strategy for dominants and the nearest neighbor rule for subordinates). These results suggest that, like humans, vervets may solve multidestination routing problems by applying simple, adaptive, context-specific "rules of thumb." The heuristics that were consistent with vervet paths in this study are the same as some of those asserted to be used by humans. These spatial movement strategies may have common evolutionary roots and be part of a universal mental navigational toolkit. Alternatively, they may have emerged through convergent evolution as the optimal way to solve multidestination routing problems.

The evolution of animal communication is a complex issue and one that attracts much research and debate. 'Receiver psychology' has been highlighted as a potential selective force, and we review how avian psychological processes and biases can influence the evolution and design of signals as well as the progress that has been made in testing these ideas in behavioural studies. Interestingly, although birds are a focal group for experimental psychologists and behavioural ecologists alike, the integration of theoretical ideas from psychology into studies of communication has been relatively slow. However, recent operant experiments are starting to address how birds perceive and respond to complex natural signals in an attempt to answer evolutionary problems in communication. This review outlines how a psychological approach to understanding communication is useful, and we hope that it stimulates further research addressing the role of psychological mechanisms in signal evolution.

ObjectiveDecision-making, when providing care and treatment for a person with dementia at the end of life, can be complex and challenging. There is a lack of guidance available to support...

Simple heuristics that make us smart presents a valuable and valid interpretation of how we make fast decisions particularly in situations of ignorance and uncertainty. What is missing is how this intersects with thinking under even greater uncertainty or ignorance, such as novice problem solving, and with the development of expert cognition.

To evaluate the success of simple heuristics we need to know more about how a relevant heuristic is chosen and how we learn which cues are relevant. These meta-abilities are at the core of ecological rationality, rather than the individual heuristics.

With the proposed penetration of electric vehicles and advanced metering technology, the demand side is foreseen to play a major role in flexible energy consumption scheduling. On the other hand, the past several years have witnessed utility companies' growing interests to integrate more renewable energy resources. These renewable resources, for example, wind or solar, due to their intermittent nature, brought great uncertainty to the power grid system. In this article, we propose a mechanism that attempts to mitigate the grid operational uncertainty induced by renewable energies by properly exploiting demand flexibility with the help of advanced smart-metering technology. To address the challenge, we develop a novel locational marginal price (LMP)-based pricing scheme that involves active demand-side participation by casting the network objective as a two-stage Stackelberg game between the local grid operator and several aggregators. In contrast to the conventional notion that generation follows load, our game formulation provides more flexibility for the operators and tries to provide adequate incentives for the loads to follow the (stochastic renewable) generation. We use the solution concept of subgame perfect equilibrium to analyze the resulting game. Subsequently, we discuss the optimal real-time conventional capacity planning for the local grid operator to achieve the minimal mismatch between supply and demand with the wind power integration. Finally, we assess our proposed scheme with field data. The simulation results show that our proposed scheme works reasonably well in the long term, even with simple heuristics.

Calcium carbonate (CaCO3) scale can form through an "auto-scaling" process in production systems with a CO2-rich environment due to fluid (water/oil/gas) depressurisation. Thermodynamic modelling is used to estimate the amount and severity of CaCO3 scale precipitation in this context in order to design scale inhibitor or other types of treatments. However, field experience has indicated that thermodynamic calculations often lead to an overestimation of the calcite scale problem. One possible source of this discrepancy may be due to kinetic effects; i.e. that the calcite is somewhat oversaturated (Saturation Ratio, SR >1) but the driving force is not sufficiently large and so the deposition is kinetically "slow". The industry response to this situation has been to come up with some simple heuristics based on field observations, and "rules of thumb" have been developed to account for this apparent overestimation of calcite deposition. The central objective of this paper is to try to address the problem of using such an arbitrary field procedure for calcite scale prediction by introducing the kinetics of calcite deposition in a thermodynamically consistent manner. We view the calcite auto-scaling system as one which moves from SR < 1 (non scaling) incrementally to one that is slightly supersaturated (SR slightly > 1). By making the deposition rate a function of SR, this would give slow rate of deposition initially, but as the system moved into the more scaling regime in the production system (SR > 1) then the deposition rate would increase. However, throughout the system, this kinetic formulation must limit correctly (i.e. it must be consistent with) the underlying equilibrium thermodynamics of the full brine/oil/gas system. Thus, we replace the idea of using heuristic estimates of when calcite scaling occurs by one where an estimate (or measurement) of the kinetics is made; indeed, a range of kinetics rates can easily be run to give an envelope of calcite scaling profiles in the wellbore and throughout the production system. In this paper, we present a model that incorporates a fully consistent kinetic formulation into a general thermodynamic scale prediction model. This model can then calculates scaling profiles in production systems considering both kinetic and thermodynamic effects. In particular, a rate law for the precipitation of CaCO3 based on the respective degree of super-saturation is coupled with the Heriot-Watt FAST Scale Prediction model (HW FAST). HW FAST uses the Pitzer equations and the Peng-Robinson Equation of State to model, respectively, the aqueous and hydrocarbon phases (gas and oil), and it has been developed to calculate CaCO3 scaling profiles caused by a de-pressurisation effect in CO2-rich production systems. First, we present an equilibrium thermodynamic example calculation showing that CaCO3 scale precipitates in CO2-rich production systems due to a de-pressurisation effect, and that precipitation is more severe topsides where the pressure is low than it is downhole where the pressure is high. It is explained that the amount of scale precipitated in this auto-scaling process must be plotted as a cumulative amount, in order to avoid the calculation of a potentially misleading scaling profile. This calculation is then repeated, but also considering kinetic effects for systems with varying temperatures and water flowrates. In the example presented here, we show that this system with sufficiently "low" water flowrates can be approximated by a thermodynamic calculation, and that systems with "high water" flowrates must take kinetic effects into consideration. This scaling profile can then be used to more accurately design scale inhibitor treatments, thus avoiding under or over-treatments (e.g. opting for continuous scale inhibitor injection instead of the more expensive squeeze treatment). Our approach focuses on calculating the correct scaling profile in auto-scaling processes, both qualitatively and quantitatively, by coupling a kinetic formulation to a thermodynamic model, and it can be readily extended to other auto-scaling processes. Further, our kinetic model can be easily integrated with commonly available scale prediction software.

Because environments are temporally variable, animals may often estimate the current environmental state to inform their behavioral choices. However, using experience may cause behavior to lag behind the current state of the environment, and estimates may suffer from sampling errors. We used stochastic dynamic models to examine the environmental conditions that favor flexible rather than fixed estimates and behaviors. The examination was conducted in the context of reproductive decisions made by the female peacock wrasse (<it>Symphodus tinca</it>), a nearshore Mediterranean fish. Female peacock wrasses can choose to spawn in a nest, with males that defend these nests within territories, or out of a nest, with males that defend neither territories nor nests. A female must expend effort and time to find nesting males, and the profitability of this search, relative to spawning with nonnesting males, changes with the density of nests and relative hatching success of eggs in and out of nests. A female can increase her fitness by estimating the environment's state and matching her reproductive decisions to the current environment. These estimates can be flexible and formed by experience, or fixed and formed by selection. We found that flexible estimates based on experience do better when there is variance within and between seasons and when there is greater uncertainty. The optimal rate for forgetting experiences is set by the rate of environmental change. Comparisons of predicted female behavior using flexible and fixed estimates with observed behavior suggest that females use estimates that are updated by experience.

Understanding the neural mechanisms of intuitive decision making is an important social and economic issue which may contribute to optimizing the managerial procedures and to support the evaluation of intuitive critical decisions made under pressure of time. The review aims to discuss the cognitive and affective processes underlying intuition, the differences between the neural correlates underlying rational and intuitive process of decision making as well as the possible differences in neural mechanisms of expert versus ‘non-expert’ intuition. Since the research concerning intuition are relatively new on the ground of neuroscience, the current state of knowledge is very limited and answers for many questions are unclear. However, the following review distinguish several neural correlates that are assumed to be specific for the intuitive process, the intuitive decision making and even for the expert intuition.

Whereas classic work in judgment and decision making has focused on the deviation of intuition from rationality, more recent research has focused on the performance of intuition in real-world environments. Borrowing from both approaches, we investigate to which extent competing models of intuitive probabilistic decision making overlap with choices according to the axioms of probability theory and how accurate those models can be expected to perform in real-world environments. Specifically, we assessed to which extent heuristics, models implementing weighted additive information integration (WADD), and the parallel constraint satisfaction (PCS) network model approximate the Bayesian solution and how often they lead to correct decisions in a probabilistic decision task. PCS and WADD outperform simple heuristics on both criteria with an approximation of 88.8 % and a performance of 73.7 %. Results are discussed in the light of selection of intuitive processes by reinforcement learning.

Intuition and reasoning are two major types of human decision-making and judgments. With the development of behavioral economics and decision-making neuroscience, more and more scholars are no longer satisfied with simply studying intuitionist decision-making from the perspective of speculative and theoretical framework. Instead, they introduce the idea of experimental science into decision-making research in management science. From the aspects of behavioral science and neuroscience, the existing intuition decision-making research is summarized. The main contributions of the experimental research on the intuition decision-making mechanism and the experimental design of the intuition decision-making task are summarized. Finally, an event-based Potential (ERP) technology to determine the experimental design of intuition, designed a risk management experiment of economic management to study the neural basis behind decision-making, the use of ERP technology features high temporal resolution, focusing on the analysis of the key aspects of economic management process and Found in all aspects of treatment based on the composition of EEG.

From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and strategic decisions in diverse, high-dimensional and uncertain situations. But can these methods help us devise robust strategies for managing environmental systems under great uncertainty? Here we explore how reinforcement learning (RL), a subfield of artificial intelligence, approaches decision problems through a lens similar to adaptive environmental management: learning through experience to gradually improve decisions with updated knowledge. We review where RL holds promise for improving evidence-informed adaptive management decisions even when classical optimization methods are intractable and discuss technical and social issues that arise when applying RL to adaptive management problems in the environmental domain. Our synthesis suggests that environmental management and computer science can learn from one another about the practices, promises and perils of experience-based decision-making. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Uncertainty and Certainty.