Top PDF An Agent-based Route Choice Model

An Agent-based Route Choice Model

An Agent-based Route Choice Model

However, this trip based modeling paradigm encounters great difficulty when it tries to describe and evaluate some emerging initiatives to mitigate congestion and pollution, such as toll roads and advanced traveler information systems. For example, toll roads differentiate level of service on the supply side. As travelers who have distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g. willingness to switch routes with potential savings) adjust their travel decisions in a differentiated market, the network can exhibit new traffic patterns, which will further affect pric- ing strategies and investment decisions. Traditional trip based models cannot provide an accurate description of this complex mechanism, as they are inca- pable of addressing important issues such as equity. Actually, many researchers (Estache and Strong, 2000; Yang and Zhang, 2002; Santos and Rojey, 2004) have emphasized the importance of equity as a consequence of road pricing and pointed out equity is an individual, not a group, problem (Evans, 1992). To account for this complexity, transportation economists and policy makers have long advanced their focus from first-best prices with homogeneous network as- sumptions to second-best prices under heterogeneity in both network users and service providers (Pigou, 1920; Knight, 1924; Mohring and Harwitz, 1962; Vick- rey, 1963; Button and Verhoef, 1998; Levinson, 2005; Zou and Levinson, 2006), which requires explicitly modeling individual travelers route choice behavior. Although some researchers have studied these problems on small networks, a behavior-based model, which is not only sufficiently accurate but also applicable on large network, does not exist (Zhang, 2006).
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Latent variables and route choice behavior

Latent variables and route choice behavior

These results confirm previous findings found in the travel behavior literature. Model estimates suggest that latent variables alter the perception of alternative attributes by travelers (e.g., habit leads to choosing longer routes), as hypothesized in the conceptual framework proposed by Bovy and Stern (1990) to describe the route choice process as dependent not only on route attributes, but also on spatial abilities, behavioral patterns and driving preferences of travelers. Also, the significance of the latent factors generalize to route choice from revealed preferences findings about route choice in simulation experiments, performed for example by Polydoropoulou et al. (1995), who determined the influence of attitudes on route choice diversions, Bogers et al. (2005), who analyzed the effect of learning and habit in a simulation of selection between two routes, and Parkany et al. (2006), who illustrated that attitudes affect consistency and diversion in the choice of paths. Moreover, the relevance of habit and familiarity agrees with the theory that behavior really has core preferences based on habitual behavior and contingent preferences based on context (Fujii and Garling 2003).
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One route or the other? : development and evaluation of a day to day route choice model incorporating the principljes of inertial behavior and quantification of the indifference band based on a real world experiment

One route or the other? : development and evaluation of a day to day route choice model incorporating the principljes of inertial behavior and quantification of the indifference band based on a real world experiment

Figure 45 visualizes the individual-specific indifference bands found by the data-analysis (conscious and subconscious indifference bands) and 2-step-model (conscious indifference bands). In general, the 2-step-model found relatively high indifference bands compared to the indifference bands found by the data-analysis. An explanation for this may lie in the fact that the indifference bands found by the 2-step-model are based on all observations in which an inertial choice is possible, while the indifference bands found in the data-analysis are based on only those observations in which an inertial choice is actually made or an individual actually stated that he was indifferent for the travel time difference between the two route alternatives. Note that especially the conscious indifference band of individual 122 (and to a lesser extend individual 223) obtained by the 2-step-model is very high. One could consider these to be outliers. They occur because the coefficients of the inertia sub- model are estimated for all individuals together. In combination with the characteristics of this individual 122 (or individual 223) somewhat extreme results are obtained. Remark that these high values are not obtained by the data-analysis for these specific individuals. That might be the case since within the modeled expected travel times on which the inertia thresholds in this data-analysis are based simply do not contain these big differences. As a result this analysis obtained only threshold minimums; when more runs are made, higher thresholds might be obtained. A closer look at these individuals within the dataset shows that they first try both route alternatives and after that stick to their preferred route alternative without making any switches during the remaining experimental runs. So, these individuals have developed a strong preference for a certain route alternative (i.e. they belong strongly to driver type 2). If this is the longer route alternative for some OD-pairs, this results in a high likelihood of this individual to perform inertial behavior leading to a high inertia threshold. It is likely that in a total population, individuals exposing this behavior are more common. Therefore, it is chosen to keep these values within this research.
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STREETS: an agent-based pedestrian model

STREETS: an agent-based pedestrian model

Socio-economic characteristics relate to income and gender, and are used to create an activity schedule for the agent, that is a sequence of locations which the agent intends to visit once in the town centre. This schedule is refined using shortest-path determination on the street network so that the agent has a pre-determined plan, which defines a route that it intends to take in the model. It is envisaged that this phase of the model will be enhanced considerably in later versions of STREETS, perhaps introducing the travelling salesman algorithm, for example. For example, provision will be made for agents with different levels of knowledge about the urban centre, and hence non-optimal paths from location to location.
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Behavioural Considerations in Route Choice Modelling

Behavioural Considerations in Route Choice Modelling

(McFadden, 1978). IAL incorporates a full probabilistic choice set generation approach, which is inapplicable in route choice modelling (Prato, 2009b). For instance, considering a small dataset of 10 alternatives, a selection probability has to be calculated for every 1023 non-empty subsets of alternatives, which is very time-consuming and impractical, and (3) the nested structure allows the consideration of multiple anchor points and their effects on route choice decisions. Also, the inclusion of multiple landmarks and anchor-points, and the consideration of several forms of heterogeneity, such as decision makers’ taste variations, are much more manageable and can be accommodated more easily in LK than in the NL mode. This is due to the flexible structure of the error term, which can approximate almost any desirable error structure (Walker et al., 2004). This study contributes to the existing literature in two ways. First, it improves the behavioural, theoretical, and practical aspects of anchor-based route choice models by capturing the effects of both anchor points and route level attributes within a nested choice model framework, and clearly underscores the importance of considering the effects of anchor points in conjunction with route- level attributes. Second, a large real-world road network, consisting of over 40,000 nodes and 19,000 links, has been studied and a MH algorithm has been adopted to generate a set of considered alternatives. To the best of the authors’ knowledge, the largest network previously tested on this algorithm was composed of about 8,000 nodes and 17,000 links (Flötteröd & Bierlaire, 2013). The major advantage of MH sampling algorithm over conventional methods (e.g. link labelling, link elimination, etc.) is that it provides researchers with path sampling probabilities, so that model estimates based on these sets are not biased. It is noted that in route-based models and most of the link-based formulations, the consideration set is commonly generated using shortest-path algorithms with some pre-defined impedance function, which do not provide path sampling probabilities, and hence do not account for the correlation between sampled and non-sampled paths, resulting in biased estimates.
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ROUTE CHOICE MODELLING FOR BICYCLE TRIPS

ROUTE CHOICE MODELLING FOR BICYCLE TRIPS

Abstract: This paper intends to find out which parameters have the highest influence on bicyclists’ route-choice-behaviour and how they contribute. There are many attributes considered throughout different studies that are favoured by cyclists. The attributes are the basis for generating a function which predicts the route choice of cyclists. The paper aims at making a comparison between distance-based routes and attribute-based routes. The model for creating the bicycle route choice program is based on the network model of Norrköping, Sweden. The preferred attributes of the model each assign a weight in the cost function of the model for each link of the urban network. The algorithm of the lowest cost function route searches the shortest path in terms of assigned link costs over the whole network. For comparing the results of the cost function and the shortest route (between an origin-destination), the model has a shortest path finding algorithm between different Origin and Destination pairs implemented. It has been viewed that around 25% of all cumulative routes do not show any distance differences between the shortest path distance and attributes based solution. With the used weights of the Standard scenario, it can be seen that the trade-off between shortest distance and safety respectively comfort can be optimized, fulfilling both criteria (distance and safety/comfort).
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The Day-to-Day Dynamics of Route Choice

The Day-to-Day Dynamics of Route Choice

where the history parameters may be the parameters of the posterior distribution (in fact, three would be adequate) or recently experienced travel times (in the case of Model B formulation). In order to ease the burden on storage space (and possibly save some time in retrieving the relevant array elements), an individual-independent `typical' range for each of the history parameters could be stored, with the parameters relating to a particular individual only saved if they fall outside the typical range. Equally, the history parameters could be stored only approximately, according to a grouped data approach. In addition, computer memory compression techniques may be adopted to make best use of the resources. The quality of these approximations need, of course, to be investigated. If the storing of all such experiences proves infeasible, then the use of some kind of weighting scheme (à la Horowitz) may be the only alternative, despite the problem of calibrating the weights. It is notable that Cascetta (1989) appears to have found it feasible to implement an averaging process of the type described in Model B in the current paper, based on seven previous days. This was achieved using the within-day static model STODYN, applied to a realistic size network. Later, however, in thw within- day dynamic (yet still macroscopic) STODYN2, experiences were formed using weighting schemes. It is not clear to what extent this change of methodology was due to computational/storage considerations.
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An Agent Based Approach for Sinkhole Preventive Route formation in Mobile Network

An Agent Based Approach for Sinkhole Preventive Route formation in Mobile Network

on the incentive evaluation at node level. Author[9] has used the node detection method and collusion resistant incentive routing for the opportunistic routing. The node behavior was analyzed in a controlled manner to identify the safe and unsafe nodes over the network. Once the attacker node is identified, the reliable communication is performed over the network. The node level optimization in the distributed network is performed. The node level mitigation was provided for improving the network communication. Author[10] has applied the constraint driven analysis on the sensor nodes to generate the safe communication route against the sinkhole attack. The security driven improvement was achieved by the author. The node level evaluation was provided to generate the safe communication in the network. Author[11] has defined a probabilistic and mathematical model to analyze the communication behavior of node. Based on this the sinkhole detection and prevention based route formation was provided. Author[12] has used the security driven analysis to the mobile network. The threat drive observation on different communication parameter was provided by the author and later on applied the evaluation at node level to generate the safe communication path. The method has reduced the loss rate and improve the network effectiveness.
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A Dynamic Pedestrian Route Choice Model Validated in a High Density Subway Station

A Dynamic Pedestrian Route Choice Model Validated in a High Density Subway Station

The importance of route choice has also been investigated within the scientific community. Daamen (2004) provides a detailed overview of pedestrian route choice models which are based on utility maximization calculations (e.g. Gipps et al. (1986), Cheung and Lam (1998), Hughes (2000), Hoogendoorn and Bovy (2004), Daamen et al. (2005), Asano et al. (2010)). Wagoum et al. (2011) present a dynamic route choice model for pedestrian simulation in evacuation scenarios, which includes the possibility that individuals can continuously try to identify a “better” route than their current one. This is achieved using an observation principle, where pedestrians adjust their decisions based on continuous observation of their environment. Therefore, pedestrians combine the knowledge of both, local and global information, and dynamically choose the paths which are most favorable for them at the given moment.
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Which road do I take? A learning based model of route choice behavior with real time information

Which road do I take? A learning based model of route choice behavior with real time information

Several directions for future research can be indicated: First, regarding the experiment design, there is added value to expand the analysis by evaluating choice behavior on a sequence of travel time ranges and not only two points of reference (high versus low). This would provide a clearer picture of the interaction between information and travel time variability. It would be possible also to vary the mean travel times for the routes or even explore more than two routes as other simulative studies have done (Bogers et al., 2005; Bogers et al., 2007). Second, although the sample size is equivalent to the common sizes applied in behavioral research, a further investigation of the choice process should be conducted with a larger and more heterogeneous sample. This might increase both statistical power and reveal possible individual and socio-economic characteristics' effects that were insignificant in our setting. Third, important insights could well gained by
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Prediction of traveller information and route choice based on real-time estimated traffic state

Prediction of traveller information and route choice based on real-time estimated traffic state

Accurate depiction of existing traffic states is essential to devise effective real-time traffic management strategies using Intelligent Transportation Systems (ITS). Existing applications of Dynamic Traffic Assignment (DTA) methods are mainly based on either the prediction from macroscopic traffic flow models or measurements from the sensors and do not take advantage of the traffic state estimation techniques, which produce estimate of the traffic states which has less uncertainty than the prediction or measurement alone. On the other hand, research studies which highlight estimation of real-time traffic state are focused only on traffic state estimation and have not utilized the estimated traffic state for DTA applications. In this paper we propose a framework which utilizes real-time traffic state estimate to optimize network performance during an incident through traveller information system. The estimate of real-time traffic states is obtained by combining the prediction of traffic density using Cell Transmission Model (CTM) and the measurements from the traffic sensors in Extended Kalman Filter (EKF) recursive algorithm. The estimated traffic state is used for predicting travel times on alternative routes in a small traffic network and the predicted travel times are communicated to the commuters by a variable message sign (VMS). In numerical experiments on a two-route network, the proposed estimation and information method is seen to significantly improve travel times and network performance during a traffic incident.
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Financial Regulation in an Agent Based Macroeconomic Model

Financial Regulation in an Agent Based Macroeconomic Model

The model is populated by heterogeneous agents (households, firms and banks) that interact according to a fully decentralized matching mechanism. The matching protocol is common to all markets (goods, labor, credit, deposits) and represents a best partner choice in a context of imperfect information. The model is useful because it gives rise to emergent macroeconomic properties like the fluctuation of the unemployment rate, the relevance of leverage cycles and credit constraints on economic performance, the presence of bank defaults and the role of fin- ancial instability, and so on. In particular, simulations show that endogenous business cycles emerge as a consequence of the interaction between real and financial factors: when firms’ profits are improving, they try to expand the production and, if banks extend the required credit, this results in more employment; the decrease of the unemployment rate leads to the rise of wages that, on the one hand, increases the aggregate demand, while on the other hand reduces firms’ profits, and this may cause the inversion of the business cycle. Moreover,
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An Agent-Based Model of Exurban Land Development

An Agent-Based Model of Exurban Land Development

A growing interest in the underlying processes of land use and land cover patterns has led to an explosion in spatial modeling of land use change, much of it coming from outside economics (Irwin 2010). Within economics, progress has been made in estimating econometric models of land development that account for spatial interactions and heterogeneity and in extending urban spatial models to account for multiple sources of spatial heterogeneity. However, despite the preponderance of urbanization in exurban areas, very few structural models of exurban land development have been developed. Of the handful of models that have been developed, most adapt the basic urban economic model of growth by Capozza and Helsley (1989, 1990) to represent exurban household location choice and rural landowner development decisions. However, exurban land markets are not the same as urban land markets and the efficacy of applying this model to exurban regions is questionable. The urban economic model rests on the fundamental assumption of a spatial equilibrium in which land and housing rents adjust to make households indifferent to location. This equilibrium outcome depends on two fundamental assumptions of the model: first, that the number of households demanding location is very large and second, that each land parcel is a unique location (Fujita 1989). These market conditions correspond to excess demand for land at zero rents and generate equilibrium land rents that are equal to households’ maximum willingness-to-pay, their so-called bid rent, so that all gains from trade are captured by landowners.
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Enhancing Agent-Based Models with Discrete Choice Experiments

Enhancing Agent-Based Models with Discrete Choice Experiments

2.8 Lee et al. (2014) used an ABM to simulate energy reduction scenarios of owner-occupied dwellings in the UK. The agents in the model were home-owners which had to decide, triggered by certain events, if they want to carry out any energy efficiency improvement in their house. The decision-making algorithm originates in DCE data from two separate studies, where the population was divided into seven clusters with similar preferences. The preferences of the agents in each cluster were distributed around the center point of the cluster to provide a heterogeneous population. The utility function of the agents are deterministic, i.e. without error component. 2.9 It is striking that where combinations of ABMs and DCEs are applied in the literature, the role of the error com- ponent and how it is modeled are often neglected or at least not explicitly mentioned. However, the error com- ponent is central to random utility theory, which is the theoretical foundation of DCEs. This paper contributes to this field by rigorously adhering to random utility theory that underlies the DCE method to improve the em- pirical foundation of ABMs.
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Line planning with user optimal route choice

Line planning with user optimal route choice

We study line planning with route choice (LPRC) as introduced in [Sch14] under the name ca- pacitated line planning with routing. We start with the formulation of the problem as a bilevel optimization problem. We then transfer the bilevel problem into a single-level IP using two dif- ferent approaches: the first one is based on the dualization of the inner-level routing problem, the second one is based on implicit shortest-path constraints and was first described in [Sch14]. In both cases, the resulting IP model can be read as a line planning with route assignment (LPRA) model with additional constraints and variables. In particular because some of these variables are binary, and the constraints contain ’big-M’-values, the resulting models are harder to solve than LPRA when using standard solvers. However, based on the observation that most of the added constraints may not be necessary, we develop a constraint-generation method which starts with solving LPRA, and iteratively detects violated constraints and adds them to the model. We conduct extensive experiments in which we compare both models for LPRC. Furthermore, we compare the obtained solutions to LPRA solutions to investigate how often LPRA indeed leads to solutions which violate the assumption that passengers travel on shortest paths, and by how much these are violated. This allows us to comment on the trade-off between solution quality and solution time when comparing LPRC and LPRA. To solve larger problems, we propose a multi-objective genetic algorithm, which is able to find trade-off solutions between travel time and line concept costs for real-world sized instances in reasonable time.
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The impact of travel information's accuracy on route choice

The impact of travel information's accuracy on route choice

likelihood of choosing the reliable route as demonstrated also by the aggregate analysis which implies greater tendency towards risk aversion when uncertainty increases. Moreover, when accuracy is low, even a useless alternative (R2) may appear attractive. Here the payoff variability effect would suggest that the high level of variability in the choice environment is inhibiting learning and causing greater confusion. Third, the model suggests that predominantly risk averse participants are, ceteris paribus, less likely to choose the shorter and riskier route. Thus for predominantly risk averse travellers, suggestions indicating a risky route would be likely ignored. Fourth, the model asserts that travellers will have greater sensitivity to descriptive information compared to feedback information based on experience and foregone payoffs demonstrating the importance of information. Fujii & Kitamura (2000) also showed greater sensitivity to information compared to experience, and Ben-Elia & Shiftan (2010) verified this for the short run when knowledge about the network performance is relatively low. However, more trials (than the twenty in our case) are needed to verify this result.
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Route choice modelling in dynamic traffic assignment

Route choice modelling in dynamic traffic assignment

In this form a direct relationship can be seen between the scaling parameter µ and the travel cost. The only unknown parameter is a proportionality factor θ . Instead of calibrating each routeset, now the proportionality factor has to be calibrated. At first glance this might not seem to be an improvement. However, since θ is derived from travel cost variance the parameter can be linked to user classes, trip characteristics and link type. Further, the proportionality factor can be influenced in the model based on the level of information of travellers. This new approach is considered significantly more flexible for application and easier to apply to characteristics of model elements (network links, user classes, dynamic measures, etc.). However, additional research is needed to see to what extent this new method can be implemented for large scale modelling and to what extent it performs better than a scale parameter for each routeset. Elements that should be covered in that research are how multiple proportionality factors can be used to determine the scale parameter for one routeset.
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Model-based evolution of collaborative agent-based systems

Model-based evolution of collaborative agent-based systems

There have been a number of projects that have ad- dressed multi-agent systems (MAS) and MDA before and since our first research effort using this technology back in 2003 [8]. As we investigated more and more de- tailed issues, we found leverage using MDA principles [9]. We targeted a model-based engineering approach flexible enough for many model representations (ranging from abstract requirements to concrete code) to be used. While we engaged the MDA structure (i.e., CIM to PIM to PSM) to separate key development and evolution con- cerns, a distinguishing element of our approach is that we have not stayed strictly with the OMG route of de- riving artifacts mostly in UML. Rather, when UML was the expedient route, it was used. Otherwise, we adopted the notion that if we had a close rendering of the capa- bilities needed in the lower levels and they could be ab- stracted to the higher layer without creating an interoper- ability dependency, then we would build the transforms and mappings directly. For example, when we developed the Expander for flexible tasking (described earlier), it was more expedient to opportunistically map the notion of task in the PIM to the agent tasking components in Cougaar (without violating the PIM and PSM separation). This allowed quick and verifiable transformation rules.
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Agent–Based Keynesian Macroeconomics   An Evolutionary Model Embedded in an Agent–Based Computer Simulation

Agent–Based Keynesian Macroeconomics An Evolutionary Model Embedded in an Agent–Based Computer Simulation

Turning back to the ‘expected utility function’: The maximization is subject to an intertemporal budget constraint, which leads to the well–known ‘Euler equation’ as the optimality condition. This approach generates ‘precautionary motives’ for savings. Usually the optimization problem must be solved by ‘dynamic programming’, i.e. ‘backward induction’. As mentioned above in chapter 1, it can be assumed that households do not follow such a hyper–rational optimization: This can be due to limited computational power, or at least due to the fact that in reality agents face ‘true uncertainty’—so that they cannot calculate expected utility. Finally, individuals can have various savings objectives, which differ from consumption smoothing. For example bequest motives, pres- tige, economic power, or safety can also justify the accumulation of wealth (Frietsch, 1991). Usually, models of optimal ‘intertemporal choice’ do not incorporate such motives. Moreover, macroeconomic and microeconomic evidence offer some support for the view that individuals follow rule–of–thumb behavior (Shefrin and Thaler, 1988; Campbell and Mankiw, 1989; Loewenstein, 1988). Several experimental studies identify that individuals do not perform ‘backward induction’ to solve such complex ‘intertemporal’ decision problems in a rational way (Anderhub, 1998; Carbone and Hey, 1997; Hey and Dardanoni, 1988). The most important reason is that people have limited compu- tational power. Hence there is empirical and experimental evidence that justifies that we do not employ hyper–rational optimizing behavior: We follow this intention and design the savings behav- ior through rule–of–thumb behavior. Surely, it could be the task for further research to develop a more sophisticated yet better manageable approach. To note, it could be fruitful to use the results of future experimental studies in order to define reasonable savings heuristics.
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Agent–Based Keynesian Macroeconomics - An Evolutionary Model Embedded in an Agent–Based Computer Simulation

Agent–Based Keynesian Macroeconomics - An Evolutionary Model Embedded in an Agent–Based Computer Simulation

Turning back to the ‘expected utility function’: The maximization is subject to an intertemporal budget constraint, which leads to the well–known ‘Euler equation’ as the optimality condition. This approach generates ‘precautionary motives’ for savings. Usually the optimization problem must be solved by ‘dynamic programming’, i.e. ‘backward induction’. As mentioned above in chapter 1, it can be assumed that households do not follow such a hyper–rational optimization: This can be due to limited computational power, or at least due to the fact that in reality agents face ‘true uncertainty’—so that they cannot calculate expected utility. Finally, individuals can have various savings objectives, which differ from consumption smoothing. For example bequest motives, pres- tige, economic power, or safety can also justify the accumulation of wealth (Frietsch, 1991). Usually, models of optimal ‘intertemporal choice’ do not incorporate such motives. Moreover, macroeconomic and microeconomic evidence offer some support for the view that individuals follow rule–of–thumb behavior (Shefrin and Thaler, 1988; Campbell and Mankiw, 1989; Loewenstein, 1988). Several experimental studies identify that individuals do not perform ‘backward induction’ to solve such complex ‘intertemporal’ decision problems in a rational way (Anderhub, 1998; Carbone and Hey, 1997; Hey and Dardanoni, 1988). The most important reason is that people have limited compu- tational power. Hence there is empirical and experimental evidence that justifies that we do not employ hyper–rational optimizing behavior: We follow this intention and design the savings behav- ior through rule–of–thumb behavior. Surely, it could be the task for further research to develop a more sophisticated yet better manageable approach. To note, it could be fruitful to use the results of future experimental studies in order to define reasonable savings heuristics.
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