Key Model Predictions Research Articles

Experience-Based Discrimination

I study discrimination arising from individual experiences of employers with worker groups. I present a model in which employers are uncertain about the productivity of one of two groups and learn through hiring. Positive experiences lead to positive biases, which correct themselves by leading to more hiring and learning. Negative experiences decrease hiring and learning, preserving negative biases, which can cause persistent discrimination. The model explains prejudice as incorrect statistical discrimination and generates novel predictions and policy implications. I then illustrate experience-based discrimination in an experimental labor market, finding support for key model predictions. (JEL D83, J23, J24, J31, J71, M51)

Nutrient Dynamics in a Coupled Terrestrial Biosphere and Land Model (ELM‐FATES‐CNP)

AbstractWe present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon‐nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre‐existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant‐soil dynamics for a large number of size‐ and functional‐type‐resolved plant cohorts within a time‐since‐disturbance‐resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high‐light‐availability canopy positions allocate more carbon to fine roots than plants in low‐light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon‐nutrient coupling with vegetation demography within Earth system models.

Inspiratory and sigh breathing rhythms depend on distinct cellular signalling mechanisms in the preBötzinger complex.

Breathing behaviour involves the generation of normal breaths (eupnoea) on a timescale of seconds and sigh breaths on the order of minutes. Both rhythms emerge in tandem from a single brainstem site, but whether and how a single cell population can generate two disparate rhythms remains unclear. We posit that recurrent synaptic excitation in concert with synaptic depression and cellular refractoriness gives rise to the eupnoea rhythm, whereas an intracellular calcium oscillation that is slower by orders of magnitude gives rise to the sigh rhythm. A mathematical model capturing these dynamics simultaneously generates eupnoea and sigh rhythms with disparate frequencies, which can be separately regulated by physiological parameters. We experimentally validated key model predictions regarding intracellular calcium signalling. All vertebrate brains feature a network oscillator that drives the breathing pump for regular respiration. However, in air-breathing mammals with compliant lungs susceptible to collapse, the breathing rhythmogenic network may have refashioned ubiquitous intracellular signalling systems to produce a second slower rhythm (for sighs) that prevents atelectasis without impeding eupnoea. KEY POINTS: A simplified activity-based model of the preBötC generates inspiratory and sigh rhythms from a single neuron population. Inspiration is attributable to a canonical excitatory network oscillator mechanism. Sigh emerges from intracellular calcium signalling. The model predicts that perturbations of calcium uptake and release across the endoplasmic reticulum counterintuitively accelerate and decelerate sigh rhythmicity, respectively, which was experimentally validated. Vertebrate evolution may have adapted existing intracellular signalling mechanisms to produce slow oscillations needed to optimize pulmonary function in mammals.

A bifurcation integrates information from many noisy ion channels and allows for milli-Kelvin thermal sensitivity in the snake pit organ

In various biological systems, information from many noisy molecular receptors must be integrated into a collective response. A striking example is the thermal imaging organ of pit vipers. Single nerve fibers in the organ reliably respond to milli-Kelvin (mK) temperature increases, a thousand times more sensitive than their molecular sensors, thermo-transient receptor potential (TRP) ion channels. Here, we propose a mechanism for the integration of this molecular information. In our model, amplification arises due to proximity to a dynamical bifurcation, separating a regime with frequent and regular action potentials (APs), from a regime where APs are irregular and infrequent. Near the transition, AP frequency can have an extremely sharp dependence on temperature, naturally accounting for the thousand-fold amplification. Furthermore, close to the bifurcation, most of the information about temperature available in the TRP channels' kinetics can be read out from the times between consecutive APs even in the presence of readout noise. A key model prediction is that the coefficient of variation in the distribution of interspike times decreases with AP frequency, and quantitative comparison with experiments indeed suggests that nerve fibers of snakes are located very close to the bifurcation. While proximity to such bifurcation points typically requires fine-tuning of parameters, we propose that having feedback act from the order parameter (AP frequency) onto the control parameter robustly maintains the system in the vicinity of the bifurcation. This robustness suggests that similar feedback mechanisms might be found in other sensory systems which also need to detect tiny signals in a varying environment.

Social media feedback and extreme opinion expression

On popular social media platforms such as Twitter, Facebook, Instagram, or Tiktok, the quantitative feedback received by content producers is asymmetric: counts of positive reactions such as ‘likes,’ or ‘retweets,’ are easily observed but similar counts of negative reactions are not directly available. We study how this design feature of social media platforms affects the expression of extreme opinions. Using simulations of a learning model, we compare two feedback environments that differ in terms of the availability of negative reaction counts. We find that expressed opinions are generally more extreme when negative reaction counts are not available than when they are. We rely on analyses of Twitter data and several online experiments to provide empirical support for key model assumptions and test model predictions. Our findings suggest that a simple design change might limit, under certain conditions, the expression of extreme opinions on social media.

A tractable overlapping generations structure for quantitative DSGE models

This paper develops a novel tractable overlapping generations (OLG) structure whose aggregate equations resemble a model of an infinitely lived representative agent, except that there are no aggregate transversality conditions in the OLG economy. The main assumptions are complete markets and time-invariant (but age-dependent) consumption shares of age-groups. The tractability of the OLG structure here distinguishes it from conventional OLG models – the present structure is suitable for quantitative dynamic stochastic general equilibrium (DSGE) macro models. Importantly, the OLG structure here maintains key predictions of standard OLG models, namely the possibility of low real interest rates and of equilibrium indeterminacy. • This paper develops a novel tractable overlapping generations (OLG) structure. • Aggregate equations resemble a model of an infinitely lived representative agent. • However, there are no aggregate transversality conditions in the OLG economy. • The OLG structure here is much more tractable than conventional OLG models. • Thus, the present OLG structure is suitable for quantitative DSGE macro models.

A computational theory of evaluation processes in apathy

Computational modelling can offer valuable insight on mental illness. However, this approach has rarely been adopted to investigate apathy, a condition characterising a variety of psychiatric and neurological syndromes. This paper proposes a computational model of apathy and tests key model predictions in the healthy adult population. Building upon recent reference-dependent theories of evaluation, the model interprets apathy as arising from an excessive uncertainty about the distribution of incentives in the environment. This predicts that high-apathy individuals appraise the value of stimuli as less extreme and as more similar to one another. These predictions were assessed in two online studies where healthy adults rated the value of pictures characterised by varying levels of emotional salience. In line with the model, we observed that high-apathy individuals perceive negative stimuli as less negative, positive stimuli as less positive, and discriminate less among stimuli characterised by different salience. The contribution of this paper is twofold. On a more specific level, it sheds light on the precise mechanisms underlying evaluation processes in apathy. On a more general level, it highlights the insight offered by models of reference-dependent evaluation for understanding psychopathology.

The promise and peril of dynamic targeted pricing

Dynamic pricing is widely adopted in many industries, such as travel and insurance. These industries are also gaining extensive capabilities in identifying and segmenting customers, partly fueled by the increasing availability of data. It is natural to ask whether firms should take advantage of such developments by charging different prices to different customer segments. If so, under what conditions? We seek answers to these highly managerially relevant questions.We consider a market with two customer segments served by a monopolist. The monopolist can choose among a set of pricing strategies to exploit consumers’ inter-temporal preferences and/or inter-segment variations. At one end of the spectrum, the firm can charge a constant price to all customers, which is called static pricing. At the other end of the spectrum, the firm can charge different prices to different customer segments and vary these prices over time, which is referred to as dynamic targeted pricing. We systematically compare these alternative pricing strategies. We show that dynamic pricing without targeting can be more effective than static targeted pricing when customers are not very forward looking, which corroborates the findings in the empirical literature. Interestingly, we find that the monopolist can be worse off when she adopts targeting in addition to dynamic pricing. We conduct laboratory experiments to test several key model predictions. The studies show that individuals behave in a manner consistent with the predictions of our model.

PATH-10. MULTI-OMICS PROFILING AND MACHINE LEARNING PROPOSE A REFINED MOLECULAR CLASSIFICATION SYSTEM FOR GLIOMA

Abstract Accurate molecular stratification of glioma patients is key to an optimal design of therapeutic strategy to maximize patient survival. Here we leveraged multi-omics analysis of glioma and detailed clinical follow-up to build a refined classification system for glioma patients using support vector machines. The model input included the number of non-synonymous mutations in cancer driver genes, the number of non-synonymous mutations in cancer related genes, the transcriptomic grouping information, the immune infiltrations predicted by RNA-seq dataset, the site of tumor occurrence, as well as other well-known markers including IDH mutation status and 1p19q co-deletion status. We validated key model predictions using TCGA and CGGA datasets. Our refined classification system outperforms current state-of-the-art framework used in clinic. Taken together, we propose a refined molecular classification for glioma combining multi-omics profiling and machine learning approaches.

Strong sensitivity of watershed-scale, ecohydrologic model predictions to soil moisture

It is currently unclear how changes to modeled soil water simulations influence key predictions in complex, ecohydrologic models, particularly beyond hydrology. We assessed sensitivity to soil moisture in the Soil and Water Assessment Tool by artificially perturbing simulated soil moisture and analyzing targeted prediction change. Results demonstrated a strong, process-dependent sensitivity of model predictions to perturbations in soil moisture. At the basin scale, the absolute values of differences from a baseline simulation for surface runoff, evapotranspiration, and nitrate loss ranged from 42% to 107%, 29%–76%, and 19%–369%, respectively, for soil moisture changes between 0.5 and 2 standard deviations. Landscape-scale results illustrated the impact of modeled land management actions, notably fertilizer applications, on determining the magnitude of the change. The findings highlight and motivate the need to improve soil moisture simulations, suggesting possible accuracy improvement for key model predictions by more precisely simulating soil moisture.

Sectoral allocation and macroeconomic imbalances in EMU

We document how, over 1996–2008, large capital inflows in Southern Europe coincided with broad-based growth of the nontradable sector, extending beyond the construction and real estate sectors. We then present a tractable two-sector, two-region (‘North’ and ‘South’) model of a monetary union, in which we show how the sharp, permanent, fall in Southern real interest rates that occurred in the run-up to EMU can explain the Southern consumption boom, wage growth, growth of the nontradable sector, and deteriorating external position. Upward pressure on the EMU-wide interest rate induces an opposite process in North. Consequently, both real exchange rates and external positions of the two regions diverge. Including a third country with a flexible exchange rate vis-à-vis the euro amplifies the effects of monetary integration in South, while dampening them in North. We confirm the key model predictions using a panel-BVAR for the euro area and investigates various policy reforms to facilitate the ongoing rebalancing process in the eurozone.

Strategic Implications of Confirmation Bias‐Inducing Advertising

Confirmation bias, a well‐established behavioral anomaly, asserts that when product experience is ambiguous, it is assimilated consistent with expectations set up by prior advertising. In this paper, we combine a strategic model with laboratory experiments to study the effects of consumers’ confirmation bias on firms’ advertising and pricing strategies and its implications for firms’ profits. Our results suggest that confirmation bias does not improve firms’ profits in the short run. However, it confers benefits to products that are frequently purchased in the longer time horizon. We also show that confirmation bias‐inducing advertising can have an inverse relationship with the degree of product differentiation. Furthermore, we show that our results are robust whether confirmation bias is positive or negative, whether consumers have perfect memory or suffer from memory loss, and whether the price premium induced by confirmation bias is fixed or endogenous. Our laboratory experiments test some of these key model predictions. The studies show that individuals behave in a manner consistent with the predictions of our model.

A new model of the coupled carbon, nitrogen, and phosphorus cycles in the terrestrial biosphere (QUINCY v1.0; revision 1996)

Abstract. The dynamics of terrestrial ecosystems are shaped by the coupled cycles of carbon, nitrogen, and phosphorus, and these cycles are strongly dependent on the availability of water and energy. These interactions shape future terrestrial biosphere responses to global change. Here, we present a new terrestrial ecosystem model, QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system), which has been designed from scratch to allow for a seamless integration of the fully coupled carbon, nitrogen, and phosphorus cycles with each other and also with processes affecting the energy and water balances in terrestrial ecosystems. This new model includes (i) a representation of plant growth which separates source (e.g. photosynthesis) and sink (growth rate of individual tissues, constrained by temperature and the availability of water and nutrients) processes; (ii) the acclimation of many ecophysiological processes to meteorological conditions and/or nutrient availability; (iii) an explicit representation of vertical soil processes to separate litter and soil organic matter dynamics; (iv) a range of new diagnostics (leaf chlorophyll content; 13C, 14C, and 15N isotope tracers) to allow for a more in-depth model evaluation. In this paper, we present the model structure and provide an assessment of its performance against a range of observations from global-scale ecosystem monitoring networks. We demonstrate that QUINCY v1.0 is capable of simulating ecosystem dynamics across a wide climate gradient, as well as across different plant functional types. We further provide an assessment of the sensitivity of key model predictions to the model's parameterisation. This work lays the ground for future studies to test individual process hypotheses using the QUINCY v1.0 framework in the light of ecosystem manipulation observations, as well as global applications to investigate the large-scale consequences of nutrient-cycle interactions for projections of terrestrial biosphere dynamics.

Sum of the Parts? An Empirical Test of Efficiency Advantage and Rivalry Restraint

Resource based theory and industrial organization economics are key conceptual and empirical anchors used to examine firm performance. Some of their key insights are that competitive advantage (i.e. cost efficiencies) and rivalry restraint (i.e. horizontal differentiation) are associated with higher firm performance, yet these two mechanisms have mostly been examined independently of one another. One common theme emerging from a nascent body of analytic studies is that the interaction of competitive advantage and rivalry is important and not simple. Most of what we know stems from key model predictions and still requires empirical validation. To this end, this study empirically tests key analytic model conclusions using market-level data over nineteen years of twenty U.S. carriers competing in the domestic U.S. airline industry. Using two measures of firm performance, our validation study finds strong support for the complex and nuanced interaction of cost efficiency advantage and horizontal differentiation on firm performance detailed in prior analytic work.

Stellate Cells in the Medial Entorhinal Cortex Are Required for Spatial Learning

SummarySpatial learning requires estimates of location that may be obtained by path integration or from positional cues. Grid and other spatial firing patterns of neurons in the superficial medial entorhinal cortex (MEC) suggest roles in behavioral estimation of location. However, distinguishing the contributions of path integration and cue-based signals to spatial behaviors is challenging, and the roles of identified MEC neurons are unclear. We use virtual reality to dissociate linear path integration from other strategies for behavioral estimation of location. We find that mice learn to path integrate using motor-related self-motion signals, with accuracy that decreases steeply as a function of distance. We show that inactivation of stellate cells in superficial MEC impairs spatial learning in virtual reality and in a real world object location recognition task. Our results quantify contributions of path integration to behavior and corroborate key predictions of models in which stellate cells contribute to location estimation.

Upregulation of an inward rectifying K+ channel can rescue slow Ca2+ oscillations in K(ATP) channel deficient pancreatic islets.

Plasma insulin oscillations are known to have physiological importance in the regulation of blood glucose. In insulin-secreting β-cells of pancreatic islets, K(ATP) channels play a key role in regulating glucose-dependent insulin secretion. In addition, they convey oscillations in cellular metabolism to the membrane by sensing adenine nucleotides, and are thus instrumental in mediating pulsatile insulin secretion. Blocking K(ATP) channels pharmacologically depolarizes the β-cell plasma membrane and terminates islet oscillations. Surprisingly, when K(ATP) channels are genetically knocked out, oscillations in islet activity persist, and relatively normal blood glucose levels are maintained. Compensation must therefore occur to overcome the loss of K(ATP) channels in K(ATP) knockout mice. In a companion study, we demonstrated a substantial increase in Kir2.1 protein occurs in β-cells lacking K(ATP) because of SUR1 deletion. In this report, we demonstrate that β-cells of SUR1 null islets have an upregulated inward rectifying K+ current that helps to compensate for the loss of K(ATP) channels. This current is likely due to the increased expression of Kir2.1 channels. We used mathematical modeling to determine whether an ionic current having the biophysical characteristics of Kir2.1 is capable of rescuing oscillations that are similar in period to those of wild-type islets. By experimentally testing a key model prediction we suggest that Kir2.1 current upregulation is a likely mechanism for rescuing the oscillations seen in islets from mice deficient in K(ATP) channels.

GroEL actively stimulates folding of the endogenous substrate protein PepQ

Many essential proteins cannot fold without help from chaperonins, like the GroELS system of Escherichia coli. How chaperonins accelerate protein folding remains controversial. Here we test key predictions of both passive and active models of GroELS-stimulated folding, using the endogenous E. coli metalloprotease PepQ. While GroELS increases the folding rate of PepQ by over 15-fold, we demonstrate that slow spontaneous folding of PepQ is not caused by aggregation. Fluorescence measurements suggest that, when folding inside the GroEL-GroES cavity, PepQ populates conformations not observed during spontaneous folding in free solution. Using cryo-electron microscopy, we show that the GroEL C-termini make physical contact with the PepQ folding intermediate and help retain it deep within the GroEL cavity, resulting in reduced compactness of the PepQ monomer. Our findings strongly support an active model of chaperonin-mediated protein folding, where partial unfolding of misfolded intermediates plays a key role.

A review of the carbohydrate-insulin model of obesity.

The carbohydrate–insulin model of obesity theorizes that diets high in carbohydrate are particularly fattening due to their propensity to elevate insulin secretion. Insulin directs the partitioning of energy toward storage as fat in adipose tissue and away from oxidation by metabolically active tissues and purportedly results in a perceived state of cellular internal starvation. In response, hunger and appetite increases and metabolism is suppressed, thereby promoting the positive energy balance associated with the development of obesity. Several logical consequences of this carbohydrate–insulin model of obesity were recently investigated in a pair of carefully controlled inpatient feeding studies whose results failed to support key model predictions. Therefore, important aspects of carbohydrate–insulin model have been experimentally falsified suggesting that the model is too simplistic. This review describes the current state of the carbohydrate–insulin model and the implications of its recent experimental tests.

A review of the carbohydrate-insulin model of obesity.

The carbohydrate-insulin model of obesity theorizes that diets high in carbohydrate are particularly fattening due to their propensity to elevate insulin secretion. Insulin directs the partitioning of energy toward storage as fat in adipose tissue and away from oxidation by metabolically active tissues and purportedly results in a perceived state of cellular internal starvation. In response, hunger and appetite increases and metabolism is suppressed, thereby promoting the positive energy balance associated with the development of obesity. Several logical consequences of this carbohydrate-insulin model of obesity were recently investigated in a pair of carefully controlled inpatient feeding studies whose results failed to support key model predictions. Therefore, important aspects of carbohydrate-insulin model have been experimentally falsified suggesting that the model is too simplistic. This review describes the current state of the carbohydrate-insulin model and the implications of its recent experimental tests.

Development, Testing, and Validation of a Model-Based Tool to Predict Operator Responses in Unexpected Workload Transitions

One human factors challenge is predicting operator performance in novel situations. Approaches such as drawing on relevant previous experience, and developing computational models to predict operator performance in complex situations, offer potential methods to address this challenge. A few concerns with modeling operator performance are that models need to realistic, and they need to be tested empirically and validated. In addition, many existing human performance modeling tools are complex and require that an analyst gain significant experience to be able to develop models for meaningful data collection. This paper describes an effort to address these challenges by developing an easy to use model-based tool, using models that were developed from a review of existing human performance literature and targeted experimental studies, and performing an empirical validation of key model predictions.