From bounded rationality to ecological rationality

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.

BACKGROUNDMany of the reproductive decisions that humans make happen without much planning or forethought, arising instead through the use of simple choice rules or heuristics that involve relatively little information and processing. Nonetheless, these heuristic-guided decisions are typically beneficial, owing to humans' ecological rationality - the evolved fit between our constrained decision mechanisms and the adaptive problems we face.OBJECTIVEThis paper reviews research on the ecological rationality of human decision making in the domain of reproduction, showing how fertility-related decisions are commonly made using various simple heuristics matched to the structure of the environment in which they are applied, rather than being made with information-hungry mechanisms based on optimization or rational economic choice.METHODSFirst, heuristics for sequential mate search are covered; these heuristics determine when to stop the process of mate search by deciding that a good-enough mate who is also mutually interested has been found, using a process of aspiration-level setting and assessing. These models are tested via computer simulation and comparison to demographic age-at-first-marriage data. Next, a heuristic process of feature-based mate comparison and choice is discussed, in which mate choices are determined by a simple process of feature-matching with relaxing standards over time. Parental investment heuristics used to divide resources among offspring are summarized. Finally, methods for testing the use of such mate choice heuristics in a specific population over time are then described.1. IntroductionHumans make a variety of reproductive decisions throughout their lives, with the most important including who to choose as a mate, when to have offspring with that mate, and how to raise the children that result. Some of these decisions are consciously planned with great deliberation; others (probably the majority) may come about without deep thought, guided instead by simple strategies or rules of thumb and shaped by external environmental factors. And yet these common, less planned, more intuitive decisions often work out well in terms of reproductive success, leading to healthy members of the next generation. This happens despite the great uncertainty that may surround determining who will be a good mate or what is an appropriate way to provision one's children, and without needing to gather a lot of information about the possible courses of action before choosing a mate. In this paper, we outline some of the ways that simple heuristics can be used in making reproductive decisions, and how they can be studied. We begin with an overview of the perspective of ecological rationality within which this research is situated, before proceeding to cover investigations of heuristic rules for mate search and choice, parental provisioning of individual offspring, and decisions related to fertility.This research program on simple heuristics and their effectiveness in appropriate settings represents a new approach to questions in demographics. First, it builds a theoretically motivated, psychologically grounded framework for predicting and understanding population-level patterns of fertility-related behavior, based on the individual-level decision mechanisms that people actually use. Rather than the economists' vision of human decision making employing all of the available information, fully known preferences, and fully processed implications of both, simple heuristics are built on realistic assumptions of the limited information, time, and thinking that people are actually able to bring to bear on most of their choices. Second, this research program uses simulation models of these simple decision heuristics, in conjunction with empirical data from experiments and field observations, to test the effectiveness of the heuristics in different situations and their implications at the population level. …

A notion of rationality represents a structure that can be used to explain, evaluate, and recommend a certain course of action. Full rationality, independent of context, builds such a structure based on the rules of logic and statistical inference. Ecological rationality, on the other hand, focuses on the interaction between the human mind and the task environment. This chapter argues that ecological rationality can provide an operational framework for evolutionary medicine based on compatible foundational concepts. Risk literacy and numeracy methods developed on the basis of an adaptive toolbox approach constitute effective education tools. An evolutionary approach to medical education facilitates the integration of these accessible and well-retained educational tools for physicians and their patients, and can enrich public health policymaking.

This chapter introduces the theories of decision-making under uncertainty and risk of socio-technical systems. Following the historic development of the main conceptions of rationality, we start with expected utility theories and explain the rational choice (or normative) perspective. We explain how decisions under risk can be optimized consistently within the framework of the theory, and under which conditions such analyses are particularly applicable and when they are reduced to an economic cost-benefit analysis. It is then discussed why the classic theories are sometimes misused and why the normative perspective is not suitable to describe or predict actual human behavior, perception or evaluation of decisions and their outcomes under uncertainty and risk. We then outline alternative theories of decision-making, including descriptive approaches from behavioral economics (e.g. cognitive biases) as well as ecological rationality and heuristic decision making. As is discussed in this article, the normative approach is suited for optimizing decisions in a consistent manner for relatively well defined (often technical) problems, whereas the alternative theories are more suitable to predict actual human and social evaluations and behavior and can provide improved decision making in complex situations where socio-technical system parameters as well as the decision maker’s preferences are not well defined.

One of the fundamental problems in simulating the motion of sharp interfaces between immiscible fluids is a description of the transition that occurs when the interfaces merge and reconnect. It is well known that classical methods involving sharp interfaces fail to describe this type of phenomena. Following some previous work in this area, we suggest a physically motivated regularization of the Euler equations which allows topological transitions to occur smoothly. In this model, the sharp interface is replaced by a narrow transition layer across which the fluids may mix. The model describes a flow of a binary mixture, and the internal structure of the interface is determined by both diffusion and motion. An advantage of our regularization is that it automatically yields a continuous description of surface tension, which can play an important role in topological transitions. An additional scalar field is introduced to describe the concentration of one of the fluid components and the resulting system of equations couples the Euler (or Navier–Stokes) and the Cahn–Hilliard equations. The model takes into account weak non–locality (dispersion) associated with an internal length scale and localized dissipation due to mixing. The non–locality introduces a dimensional surface energy; dissipation is added to handle the loss of regularity of solutions to the sharp interface equations and to provide a mechanism for topological changes. In particular, we study a non–trivial limit when both components are incompressible, the pressure is kinematic but the velocity field is non–solenoidal (quasi–incompressibility). To demonstrate the effects of quasi–incompressibility, we analyse the linear stage of spinodal decomposition in one dimension. We show that when the densities of the fluids are not perfectly matched, the evolution of the concentration field causes fluid motion even if the fluids are inviscid. In the limit of infinitely thin and well–separated interfacial layers, an appropriately scaled quasi–incompressible Euler–Cahn–Hilliard system converges to the classical sharp interface model. In order to investigate the behaviour of the model outside the range of parameters where the sharp interface approximation is sufficient, we consider a simple example of a change of topology and show that the model permits the transition to occur without an associated singularity.

Current code division multiple access (CDMA) networks do not support soft handoffs between cell clusters serviced by different mobile switching centers (MSC's). Three novel network configurations are proposed to overcome this limitation. Configurations I and II employ crosslinks to allow diversity combining of signals. This is accomplished in Configuration III by linking the modified MSCs serving adjacent cell clusters. In Configuration I, a scheme which reduces the handoff blocking probability by lengthening the handoff transition time is proposed. Configuration II expands overlapping regions of adjacent cell clusters, thus enabling the use of different intercluster handoff regions for different handoff directions to prevent oscillations which may occur in Configuration I, while reducing the necessary number of intercluster handoffs. Configuration III fully shares the trunks to support the highest number of calls among all three configurations. A new mobility model for microcells is developed to enable dimensioning of the trunk groups in each configuration. For Configurations I and II, the optimal partitions of trunk groups into direct and crosslinks are determined by mathematical analyses and computer simulations. For Configuration III, the optimal number of trunks required to interconnect adjacent MSCs is investigated by computer simulations. The new call and handoff blocking probabilities in all of these configurations are compared.

COVID-19 is one of the most highly infectious diseases ever emerged and caused by newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has already led the entire world to health and economic crisis. It has invaded the whole universe all most every way. The present study demonstrates with a nine mutually exclusive compartmental model on transmission dynamics of this pandemic disease (COVID-19), with special focus on the transmissibility of symptomatic and asymptomatic infection from susceptible individuals. Herein, the compartmental model has been investigated with mathematical analysis and computer simulations in order to understand the dynamics of COVID-19 transmission. Initially, mathematical analysis of the model has been carried out in broadly by illustrating some well-known methods including exactness, equilibrium and stability analysis in terms of basic reproduction number. We investigate the sensitivity of the model with respect to the variation of the parameters' values. Furthermore, computer simulations are performed to illustrate the results. Our analysis reveals that the death rate from coronavirus disease increases as the infection rate increases, whereas infection rate extensively decreases with the increase of quarantined individuals. The quarantined individuals also lead to increase the concentration of recovered individuals. However, the infection rate of COVID-19 increases more surprisingly as the rate of asymptomatic individuals increases than that of the symptomatic individuals.Moreover, the infection rate decreases significantly due to increase of self-immunity rate.

The paper deals with mathematical modelling and computer simulation of a gearbox driving system with a double stage gearbox. Mathematical modelling and computer simulations are used for supporting diagnostic inference. Vibration is thought of as a signal of gear condition. It is stressed that vibration generated by gears is influenced by many factors. These factors are divided into four groups: design, production technology, operational, condition change. The condition change of a gearbox is given by gear faults that are divided into single faults such as a tooth crack or breakage or distributed faults as pitting, scuffing, and erosion. The faults are modelled in the case of a crack as a change of tooth stiffness in the case of distributed faults they are given multi-parameter functions. Simulated signals undergo signal analysis by spectrum, cepstrum, time-frequency spectrogram. It has been shown by computer simulation that single and distributed faults are identified by cepstrum. For explicit fault identification time-frequency spectrogram has to be additionally used. The computer simulation results are confirmed by analysis of measured vibration signals received from a gearbox wall/housing. The aim of mathematical modelling and computer simulation, besides finding the relationship between gear condition and vibration signal is in the future to give vibration signals for neural network training.

PurposeThis paper describes how financial professionals' behavioral biases influence their financial forecast and decision-making process. Most of the earlier studies are focused on well-developed financial markets, and little is researched about financial professionals, such as institutional investors, portfolio managers, investment advisors, financial analysts, etc., in emerging markets.Design/methodology/approachAn expert-validated questionnaire measure four prominent behavioral biases and Indian financial professionals' rational decision-making process. The final sample consists of 274 valid responses using the purposive sampling technique. IBM SPSS and AMOS structural equation modeling (SEM) software are used to build measurement and structural models, multivariate analysis including regression, factor analysis, etc.FindingsThe results provide empirical insights into the relationship between behavioral biases and the decision-making process. The results suggest that the structural path model closely fits the sample data. The presence of behavioral biases indicates that financial professionals' forecasting and decision-making is not always rational but bounded rational or irrational due to these factors. Furthermore, these biases (except overconfidence bias) have a markedly significant and positive relationship with irrational decision-making.Research limitations/implicationsIt is critical to eradicate these psychological errors, but awareness and attentiveness toward behavioral biases may help financial professionals to make informed decisions. Investors can improve their portfolio decisions and investments by recognizing their judgment errors and focusing on specific investment strategies to mitigate the impact of these biases. It is necessary to incorporate behavioral insights while developing training techniques for financial professionals. Rules of thumb, visual tools, financial coaching and implementing social-cultural elements in training programs enable financial professionals to develop simple, engaging, appealing and customized approaches for their clients.Originality/valueThis novel study is the first of this kind of research that examines the relationship between financial professionals' behavioral biases and rational decision-making process. This study significantly and remarkably provides insights into irrationality in financial professionals' decision-making.

Numerial Methods for Volterra Integral Equations with Singular Kernels

Pennaceous feathers capable of forming aerodynamic surfaces are characteristic of Pennaraptora, the group comprising birds and their closest relatives among non-avian dinosaurs. However, members of the basal pennaraptoran lineage Oviraptorosauria were clearly flightless, and the function of pennaceous feathers on the forelimb in oviraptorosaurs is still uncertain. In the basal oviraptorosaur Caudipteryx both the skeleton and the plumage, which includes pennaceous feathers forming wing-like arrangements on the forelimbs, are well known. We used mathematical analyses, computer simulations and experiments on a robot Caudipteryx with realistic wing proportions to test whether the wings of Caudipteryx could have generated aerodynamic forces useful in rapid terrestrial locomotion. These various approaches show that, if both wings were held in a fixed and laterally extended position, they would have produced only small amounts of lift and drag. A partial simulation of flapping while running showed similarly limited aerodynamic force production. These results are consistent with the possibility that pennaceous feathers first evolved for a non-locomotor function such as display, but the effects of flapping and the possible contribution of the wings during manoeuvres such as braking and turning remain to be more fully investigated.

A massively parallel architecture for a self-organizing neural pattern recognition machine

Evolutionary Consequences of Selected Locus-specific Variations in Epistasis and Fitness Contribution in Kauffman'sNKModel

Remembering the temporal order of a sequence of events is a task easily performed by humans in everyday life, but the underlying neuronal mechanisms are unclear. This problem is particularly intriguing as human behavior often proceeds on a time scale of seconds, which is in stark contrast to the much faster millisecond time-scale of neuronal processing in our brains. One long-held hypothesis in sequence learning suggests that a particular temporal fine-structure of neuronal activity - termed 'phase precession' - enables the compression of slow behavioral sequences down to the fast time scale of the induction of synaptic plasticity. Using mathematical analysis and computer simulations, we find that - for short enough synaptic learning windows - phase precession can improve temporal-order learning tremendously and that the asymmetric part of the synaptic learning window is essential for temporal-order learning. To test these predictions, we suggest experiments that selectively alter phase precession or the learning window and evaluate memory of temporal order.

Mathematical models are presented for studying the value of leadership in a team where the members interact with each other. The models are based on a leader's role of motivating each team member to perform closer to his/her maximum ability. These models include controllable parameters whose values reflect the amount of task interdependence among the workers as well as the motivational skill and variability in the skill of the leader. Confirming results--such as the fact that the skill level of the leader is a critical factor in the expected performance of the team--establish credibility in the models. Mathematical analysis and computer simulations are used to provide new managerial insights into the value of the leader--such as the fact that the skill of the leader can be more important than controlling the amount of interdependence among the team members and that having a choice of multiple leaders with no particular motivating skill is beneficial to the performance of small teams but not to large teams.