Vehicle stops and speed variations account for a large percentage of vehicle fuel losses especially at signalized intersections. Recently, researchers have attempted to develop tools that reduce these losses by capitalizing on traffic signal information received via vehicle connectivity with traffic signal controllers. Existing state-of-the-art approaches, however, only consider surrogate measures (e.g. number of vehicle stops, time spent accelerating and decelerating, and/or acceleration or deceleration levels) in the objective function and fail to explicitly optimize vehicle fuel consumption levels. Furthermore, the majority of these models do not capture vehicle acceleration and deceleration limitations in addition to vehicle-to-vehicle interactions as constraints within the mathematical program. The connectivity between vehicles and infrastructure, as achieved through Connected Vehicles technology, can provide a vehicle with information that was not possible before. For example, information on traffic signal changes, traffic slow-downs and even headway and speed of lead vehicles can be shared. The research proposed in this dissertation uses this information and advanced computational models to develop fuel-efficient vehicle trajectories, which can either be used as guidance for drivers or can be attached to an electronic throttle controlled cruise control system. This fuel-efficient cruise control system is known as an Eco-Cooperative Adaptive Cruise Control (ECACC) system. In addition to the ECACC presented here, the research also expands on some of the key eco-driving concepts such as fuel-optimizing acceleration models, which could be used in conjunction with conventional vehicles and even autonomous vehicles, or assistive systems that are being implemented in vehicles. The dissertation first presents the results from an on-line survey soliciting driver input on public perceptions of in-vehicle assistive devices. The results of the survey indicate that user-acceptance to systems that enhance safety and efficiency is ranked high. Driver–willingness to use advanced in-vehicle technology and cellphone applications is also found to be subjective on what benefits it has to offer and safety and efficiency are found to be in the top list. The dissertation then presents the algorithmic development of an Eco-Cooperative Adaptive Cruise Control system. The modeling of the system constitutes a modified state-of-the-art path-finding algorithm within a dynamic programming framework to find near-optimal and near-real-time solutions to a complex non-linear programming problem that involves minimizing vehicle fuel consumption in the vicinity of signalized intersections. The results demonstrated savings of up to 30 percent in fuel consumption within the traffic signalized intersection vicinity. The proposed system was tested in an agent-based environment developed in MATLAB using the RPA car-following model as well as the Society of Automobile Engineers (SAE) J2735 message set standards for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. The results showed how multi-vehicle interaction enhances usability of the system. Simulation of a calibrated real intersection showed average fuel savings of nearly 30 percent for peak volumes. The fuel reduction was high for low volumes and decreased as the traffic volumes increased. The final testing of the algorithm was done in an enhanced Traffic Experimental Simulation tool (eTEXAS) that incorporates the conventional TEXAS model with a new web-service interface as well as connected vehicle message set dictionary. This testing was able to demonstrate model corrections required to negate the effect of system latencies as well as a demonstration of using SAE message set parsing in a connected vehicle application. Finally, the dissertation develops an integrated framework for the control of autonomous vehicle movements through intersections using a multi-objective optimization algorithm. The algorithm integrated within an existing framework that minimizes vehicle delay while ensuring vehicles do not collide. A lower-level of control is introduced that minimizes vehicle fuel consumption subject to the arrival times assigned by the upper-level controller. Results show that the eco-speed control algorithm was able to reduce the overall fuel-consumption of autonomous vehicles passing through an intersection by 15 percent while maintaining the 80 percent saving in delay when compared to a traditional signalized intersection control. / Ph. D.

<p dir="ltr">During many biological processes, cells move through their surrounding environment by exerting mechanical forces. The mechanical forces are mainly generated by molecular interactions between actin filaments (F-actins) and myosin motors within the actin cytoskeleton. Forces are transmitted to the surrounding extracellular matrix via adhesions. In this thesis, we employed agent-based computational models to study the interactions between F-actins and myosin in the motility assay and the cell migration process. In the first project, the myosin motility assay was employed to study the collective behaviors of F-actins. Unlike most of the previous computational models, we explicitly represent myosin motors. By running simulations under various conditions, we showed how the length, bending stiffness, and concentration affect the collective behavior of F-actins. We found that four distinct structures formed: homogeneous networks, flocks, bands, and rings. In addition, we showed that mobile motors lead to the formation of distinct F-actin clusters that were not observed with immobile motors. In the second project, we developed a 3D migration model to define how cells mechanically interact with their 3D environment during migration. Unlike cells migrating on a surface, cells within 3D extracellular matrix (ECM) must remodel the ECM and/or squeeze their body through the ECM, which causes 3D cell migration to be significantly more challenging than 2D migration. Our model describes realistic structural and rheological properties of ECM, cell protrusion, and focal adhesions between cells and the ECM.</p>

The impact of engineered products is a topic of increasing concern in society. The impact of a product can fall into the categories of economic, environmental, or social impact; the last category is defined as the effect of a product on the daily lives of people. Design teams lack sufficient tools to help improve the impact of products and understand the impact of products at scale in society. This dissertation aims to provide insight and methods for improving the social, environmental, and economic impact of engineered products. The majority of the research focuses on the prediction of product impacts on society, which requires a sociotechnical approach with models that contain aspects of the product and society. This begins with the introduction of an agent-based modeling approach to predict how changes to a design will ultimately impact society. Chapter 3 performs a systematic review of the literature to identify common challenges in product social impact modeling, identifies ways to mitigate the challenges, and provides a general process to create product impact models. Guidance on a general modeling process is essential to enable the widespread use of predictive impact models in engineering design. Chapter 4, provides guidance on creating sociotechnical models using primary survey data and machine learning for impact prediction using a case study of improved cookstoves in Uganda. Chapter 5 presents a method for incorporating environmental impacts, using life cycle assessment and agent-based modeling to properly scale impacts from the functional unit level to the societal level. A limitation of life cycle assessment in the early phases of product design is the difficulty of scaling impacts from the functional unit level to the population level. Using agent-based modeling together with life cycle assessment enables an understanding of the number of functional units required at the population level; allowing for the quantification of the total population-level impact. There are often trade-offs in the social, environmental, and economic sustainability space. To characterize these sustainability trade-offs, Chapter 6 illustrates the modeling of social, environmental, and economic impacts of a product and how to quantify the product sustainability trade-space. Chapter 7, presents work on identifying quantitative factors for selecting engineering global development project locations based on the potential for social impact. Finally, Chapter 8 provides the general contributions of this work, identifies limitations, and provides direction for future work. The research presented in this dissertation is a step toward a future where predictive modeling of the social, environmental, and economic impacts of products is commonplace in engineering design.

Predicting the behavior of groups and how it emerges from the behaviours of individuals is a difficult task. Not only are individuals and their behaviors affected by the group and vice versa, but the way individuals are affected by and react to various conditions is difficult to predict due to the complex nature of human beings. However, if one could build models that sufficiently capture the behavior of individuals, it would be possible to simulate groups and make a prediction for the emergent behavior that way. Public Goods Games (PGGs) are a type of economic game that explores how individuals engage in cooperation and where different types of collective behaviors emerge. In group-based settings such as PGGs, there is a high level behavior pattern belonging to the group as a whole. In this work, we study how the group behavior emerges from the collection of behaviors belonging to individuals in the group. To this end, we create a model that predicts the emergent collective behavior in a PGG given a set of individual behaviors present within the group. We devise a classification scheme that groups individuals into a small set of phenotypes based on the behavior they exhibit in a PGG. We then create a model that simulates the long term behavior of groups playing a PGG based on the relative distribution of these phenotypes. Our simulation uses cognitive modeling with ACT-R to individually simulate each participant in a game. We find that our model is able to simulate group behavior that resembles what is seen with human participants given only the relative distribution of phenotypes. However, the model is not able to generalize to a PGG where the rules of the game are slightly changed. In modifying the distribution of phenotypes present in simulations, we found that increasing the number of cooperative individuals resulted in a stronger upward trend in group average contribution, while increasing the number of non-cooperative individuals had the opposite effect. Increasing the number of conditional cooperative individuals resulted in slowing the movement of group average contribution trend. / Att förutspå gruppers beteenden och hur dessa uppstår från individernas beteenden är svårt av flera skäl. Dels påverkar individernas beteende gruppen och vice versa, och dels är det svårt att förutspå hur individer påverkas av och reagerar på olika situationer och förhållanden på grund av människans komplexa natur. Om man kunde bygga modeller som fångar individers beteenden tillräckligt väl skulle det vara möjligt att genom simulering kunna ge förutsägelser på gruppens beteende. Public Goods Games (PGGs) är en typ av ekonomiskt spel som utforskar hur individer väljer att sammarbeta och där kollektiva beteenden kan uppstå. Inom gruppbaserade miljöer, som till exempel PGGs, finns det beteenden som tillhör gruppen i sig. I detta arbete studerar vi hur det gruppbeteendet härstammar från samlingen av individuella beteenden inom gruppen. För det skapar vi en modell som ger förutsägelser om det framväxande kollektiva beteendet i en PGG, givet kunskap om fördelningen av olika typer av individuella beteenden som finns i gruppen. För att göra detta utvecklar vi ett klassificeringssystem som grupperar individer i olika fenotyper baserat på deras uppvisade beteende i ett PGG. Vi skapar sedan en modell som simulerar detta PGG med en given grupp av individer. Våran simulering använder kognitiv modellering med ACT-R för att simulera varje enskild deltagare i ett PGG. Vi finner att vår modell simulerar gruppbeteenden som liknar det som syns med mänskliga deltagare, givet att man vet fördelningen av fenotyper i grupper. Modellen kan dock inte generalisera till ett PGG där reglerna är ändrade. När vi ändrade distributionen av fenotyper i simuleringen fann vi att ett ökat nummer av sammarbetsvilliga individer gjorde så att trenden av gruppen genomsnittliga bidrag rörde sig uppåt, medans ett ökat nummer av ej sammarbetsvilliga individer hade motsatt effekt. Då vi ökade antalet vilkorligt sammarbetsvilliga individer fann vi att det saktade ner förändringar av gruppen genomsnittliga bidrag.

Web 2.0時代強調由使用者貢獻內容，並藉由使用者的互動來創造群體智慧的價值。社會性書籤網站統合散佈在各處的網路資訊（尤其是由使用者所產生的部落格文章），承接內容的生產及閱讀，是網路內容價值鏈樞紐；另一方面，從媒體的角度來看，書籤網站可視為是web 2.0下的公民新聞守門人（引路人），以公民取代專業編輯，提供了一個完全不一樣的公民媒體運作方式。本研究針對社會性書籤網站中的內容評價推薦機制，探討其群體智慧運作情形：參考動物群體行為的運作原則，加上文獻的整理及實際案例的觀察，建構出社會性書籤網站推薦機制的模擬運作架構；並透過代理人模擬方法，來找出影響網站群體智慧運作的原則，及相關屬性設定對運作結果的影響。研究結果發現，社會性書籤網站的運作成效，可以分為篩選效果及文章更新效率，兩者之間具有魚與熊掌不可兼得的特性，並可藉由不同的閱讀策略安排來調整。基於web 2.0的特性，使用者同時扮演服務的生產者與消費者。因此，使用者閱讀文章時的閱讀策略安排，可視為是群體智慧運作中的工作分配策略。而群體智慧的運作原則中，正回饋效應可以提升篩選效果，判斷獨立性可以提升文章的更新效率，抑制與負回饋則可以使系統較為穩定。本研究除了為web 2.0網站的群體智慧經營提供具體的參考方針，多重代理人模擬的方法也可做為往後web 2.0相關研究及網站經營時的工具。 / The core spirit for web 2.0 is the contribution of users, and the creation of value through the interaction between users. Social book marking sites integrate all kind of contents on the Internet (especially those generated by users), and play the role of pivot between content production and consumption. From the aspect of media, social bookmarking site can be regarded as news gatekeeper (or gateway) in the web 2.0 era. This study focuses on the rating and recommendation mechanism of social bookmarking sites, trying to find out the effects of collective wisdom with regard to different operations. The principle of collective animal behavior and the existing operations of some social bookmarking sites are first surveyed. Then, an operational model of social bookmarking sites and its recommendation mechanism is built and used for subsequent simulation. / The research findings show that the performance of social bookmarking sites has a tradeoff between sifting effect and efficiency, and that the performance can be controlled through a job allocation strategy. The operation of 「positive feedback」in collective wisdom can lead to sifting effect, 「integrity and variability」 leads to efficiency, and 「negative feedback」, 「inhibition」 lead to system stability. This research is believed to provide some managerial guidelines for web 2.0 sites operation.

A comprehensive literature review was undertaken in order to define and assess the sustainability and resiliency characteristics associated with grass-based and confinement dairy farming. Primarily as a result of reduced input costs, grass-based dairy farming often enhances profitability over confinement systems, especially on small farms. Further, conversion of tilled soil to permanent pasture has been shown to significantly reduce harmful sediment and nutrient transport into waterways. Perennial forage also acts as a carbon sink, curtailing or even negating a grass-based farm's carbon footprint. Finally, social benefits derived from enhanced nutrition and higher quality of life are also associated with grass-based dairy farming. Given that policy goals of the State of Vermont include both bolstering farm viability and reducing farm-related runoff, two questions are then raised. What is the most effective way to incentivize the adoption of rotational grazing in Vermont? And what types of farms are best suited to its use? A series of interviews with dairy experts and farmers was conducted as a preliminary investigation into these questions. This qualitative evidence suggested that farmers generally adopted grass-based dairying after observing a peer's success with the method, suggesting that a key leverage point may be peer-based learning. A behavioral economics game was developed to evaluate the role of peer networks in facilitating decision-making under conditions of uncertainty. A computerized game platform simulated networks of small dairy farm enterprises, with participants acting as farm managers. Treatments varied the size of peer networks, as well as the inclusion of a perfectly-performing automated 'seed player.' Participants could base their decisions upon the successes of their peers. They received a cash incentive based on their farms' performance. Results indicated that players with higher numbers of peers made better economic decisions on average. The inclusion of a 'seed player' within a network, which modeled the ideal behavior, also facilitated better decision-making. Both of these correlations were statistically significant. Furthermore, the shape of the 'diffusion curve' of new adoptees confirmed literature on the dynamics of innovation diffusion. Public policy implications from this work include an increased focus on facilitating peer-to-peer learning among farmers where Best Management Practice adoption is a policy goal. To further evaluate the potential for peer learning to facilitate positive change, the Dairy Farm Transitions Agent Based Model (DFTABM) was developed. The model was calibrated using existing datasets along with the qualitative and quantitative results described above. It forecasts effects on farm profitability, attrition, and soil loss arising from varying assumptions about peer network connectivity, peer emulation, macroeconomic trends, and agri-environmental policy. Nine experimental treatments were assessed. Overall, it was found that high rates of emulation coupled with high rates of connectivity'especially targeted connectivity among smaller farms'yielded the best balance of farm viability and reduction in soil loss. The establishment of a performance-based tax credit had no clear correlation with the resulting soil loss figures predicted by the model. Policy implications from this study include the finding that direct payment schemes for reduction in environmental harm may not always have their intended effects, whereas policies that enhance peer-to-peer learning opportunities, especially among the proprietors of smaller farms, may present an effective and relatively affordable means by which to bolster farm profitability while also reducing environmental degradation.

L’objet principal de cette thèse est la reconsidération du rôle de la politique monétaire dans uneéconomie caractérisée par l’hétérogénéité des agents et leur apprentissage en rationalité limitée. Ce travail sesitue dans le prolongement d’une littérature qui se développe depuis les années 1980 sur le prise en comptede l’apprentissage des agents dans l’appréciation des conséquences des politiques monétaires (Sargent (1993),Evans & Honkapohja (2001)). La thèse vise à développer cette littérature en mobilisant une modélisation entermes de systèmes complexes (voir Miller & Page (2007)) et en l’appliquant à l’analyse des régimes de ciblagede l’inflation, en mettant notamment l’accent sur le rôle de l’ancrage des anticipations d’inflation. En partantdes modèles analytiques établis dans la littérature (voir Woodford (2003b)), la thèse développe progressivementun cadre d’analyse utilisant une modélisation computationnelle à base d’agents. En explorant rigoureusementles principaux phénomènes qui émergent de ce cadre, la thèse aborde les questions de conception de politiquesmonétaires optimales et montre comment les interactions entre les mécanismes d’apprentissage et les caractéristiquesdes régimes de ciblage de l’inflation sont cruciales pour les performances de ce régime. / The main goal of this Ph.D. dissertation is to reconsider the role of monetary policy in a learningeconomy populated by boundedly rational and heterogeneous agents. This work is in the line with the growingliterature on learning and monetary policy, which has emerged since the eighties (Sargent (1993), Evans &Honkapohja (2001)). The dissertation aims at developing that literature through a complex system modelingstrategy (see Miller & Page (2007)), applying it to the analysis of inflation targeting regimes, and especiallyhighlighting the role of the anchor of inflation expectations. Starting from the analytic models available in theliterature (see Woodford (2003b)), the dissertation gradually develops a framework using agent-based modeling.While deeply exploring the emergent phenomena in that framework, the thesis raises the issue of the design ofoptimal monetary policy rules, and emphasizes how the interplay of learning mechanisms and inflation targetingregimes is crucial for the performances of that regime.

This dissertation investigates how grammatical functions in transitive sentences (i.e., `subject' and `direct object') are distributed in written Swedish discourse with respect to morphosyntactic as well as semantic and referential (i.e., prominence-based) information. It also investigates how assignment of grammatical functions during on-line comprehension of transitive sentences in Swedish is influenced by interactions between morphosyntactic and prominence-based information. In the dissertation, grammatical functions are assumed to express role-semantic (e.g., Actor and Undergoer) and discourse-pragmatic (e.g., Topic and Focus) functions of NP arguments. Grammatical functions correlate with prominence-based information that is associated with these functions (e.g., animacy and definiteness). Because of these correlations, both prominence-based and morphosyntactic information are assumed to serve as argument interpretation cues during on-line comprehension. These cues are utilized in a probabilistic fashion. The weightings, interplay and availability of them are reflected in their distribution in language use, as shown in corpus data. The dissertation investigates these assumptions by using various methods in a triangulating fashion. The first contribution of the dissertation is an ERP (event-related brain potentials) experiment that investigates the ERP response to grammatical function reanalysis, i.e., a revision of a tentative grammatical function assignment, during on-line comprehension of transitive sentences. Grammatical function reanalysis engenders a response that correlates with the (re-)assignment of thematic roles to the NP arguments. This suggests that the comprehension of grammatical functions involves assigning role-semantic functions to the NPs. The second contribution is a corpus study that investigates the distribution of prominence-based, verb-semantic and morphosyntactic features in transitive sentences in written discourse. The study finds that overt morphosyntactic information about grammatical functions is used more frequently when the grammatical functions cannot be determined on the basis of word order or animacy. This suggests that writers are inclined to accommodate the understanding of their recipients by more often providing formal markers of grammatical functions in potentially ambiguous sentences. The study also finds that prominence features and their interactions with verb-semantic features are systematically distributed across grammatical functions and therefore can predict these functions with a high degree of confidence. The third contribution consists of three computational models of incremental grammatical function assignment. These models are based upon the distribution of argument interpretation cues in written discourse. They predict processing difficulties during grammatical function assignment in terms of on-line change in the expectation of different grammatical function assignments over the presentation of sentence constituents. The most prominent model predictions are qualitatively consistent with reading times in a self-paced reading experiment of Swedish transitive sentences. These findings indicate that grammatical function assignment draws upon statistical regularities in the distribution of morphosyntactic and prominence-based information in language use. Processing difficulties in the comprehension of Swedish transitive sentences can therefore be predicted on the basis of corpus distributions.

Dans le cadre d’une approche comportementale et compte tenu de la complexité des marchés financiers, cette thèse examine dans quelle mesure les réseaux orientés régissant l’interaction entre les investisseurs ainsi que leur comportement mimétique influencent leurs anticipations et la dynamique des prix. Nous proposons un marché artificiel d’actifs dans lequel des chartistes et des fondamentalistes opèrent et passent d’une stratégie d’investissement à une autre en fonction de leurs performances. Tout d’abord, nous étudions un réseau complet où l’interaction se fait de manière globale. Nous constatons que notre modèle révèle l’émergence de la volatilité excessive des prix lorsque les chartistes dominent le marché. Ensuite, nous portons notre attention sur des réseaux locaux où les agents se trouvent liés qu’à une partie des individus opérant dans le marché. Nous distinguons trois types de réseaux : le réseau régulier, le réseau petit monde et le réseau aléatoire. Puis, nous introduisons un nouveau modèle qui permet de contrôler (1) la direction du processus de « rewiring » des liens; (2) le caractère aléatoire du réseau et (3) l'asymétrie dans sa distribution des degrés en distinguant les stars des non-stars. Nous montrons que contrairement au degré du caractère aléatoire du réseau, l’asymétrie dans la distribution des degrés produit des effets opposés selon qu’il s’agit de « in-degree » ou de « out-degree ». Enfin, nous montrons comment ces analyses peuvent être utilisées pour produire des dynamiques de marché réalistes. Nous constatons que la présence d’un seuil d’imitation avec un coefficient de réaction élevé permet à notre modèle de reproduire les faits stylisés les plus importants / Within a behavioral approach and given the complexity of financial markets, the aim of this thesis is to examine the extent to which directed networks that governs the interaction among investors as well as their mimicking behavior influence their anticipations and the price dynamics. We propose an artificial asset market populated by chartists and fundamentalists who are allowed to switch from one trading strategy to the other according to their relative performances. Firstly, we study a fully connected network to test for a global interaction. We find that our benchmark model accounts for the emergence of excess volatility of asset prices when chartists dominate the market. Secondly, we restrict our focus to local interactions between investors. We generate a family of network structures that spans regular network, small world network and random network. Thirdly, we introduce a new model that allows us to control (1) the direction of the rewiring process of the links; (2) the randomness of the network; and (3) the asymmetry in its degree distribution by assuming that there are two classes of agents: stars and non-stars. We show that unlike the degree of the randomness of the network, the asymmetry in the degree distribution produces opposite effects depending on whether the network is outward or inward rewired. Finally, we address the question as to how this analysis can be used to produce realistic market dynamics. We find that the presence of a mimicking threshold with a high reaction coefficient provides a better approximation to the characteristics of the distribution of real returns and reproduces the most important stylized facts observed in financial time series

La présente thèse s'intéresse à l'évolution d'organisations à caractère démocratique ou ouvert, au travers de leur stabilité ainsi que d'autres caractéristiques comme leur capacité à fédérer, à satisfaire leurs membres ou pérenniser des situations de partage de risque entre agents hétérogènes. Les modèles proposés sont des modèles agents qui s'appuient sur une étude menée depuis 2004 par Juliette Rouchier sur les circuits courts agroalimentaires et particulièrement sur les Associations pour le Maintien d'une Agriculture Paysanne (AMAP) et leur structuration en réseaux d'AMAP à différentes échelles géographiques. La thèse suggère l'existence d'une tension entre la stabilité et la représentativité dans ce type d'organisations démocratiques et discute, dans plusieurs cas de figure, l'impact de différents facteurs sur cette tension comme le nombre de sujets discutés dans l'organisation, l'état d'esprit des membres, l'existence d'une communication structurée au sein de l'organisation, ou encore la répartition géographique des membres. Dans un second temps la thèse s'intéresse à des groupes de partage de risque entre agents hétérogènes, comme c'est le cas dans les AMAP entre producteurs et consommateurs. Elle suggère que l'apprentissage par les agents de leurs risques, c'est à dire de leurs préférences vis-à-vis des caractéristiques de leur organisation au cours du temps, pérennise un partage de risque complet entre des agents hétérogènes. De plus cet effet semble renforcé par l'introduction de préférences pour autrui, comme l'altruisme ou l'aversion aux inégalités. / This PhD thesis studies the evolution of organizations under democratic settings through their stability along with other characteristics like their representativeness, their capacity to satisfy their members or to ensure risk sharing agreement between heterogenous agents. Proposed models are agent based models grounded in a study, initiated by Juliette Rouchier in 2004, on short food chains and particularly on "Associations pour le Maintien d'une Agriculture Paysanne" (AMAP), the french equivalent of United States' Community Supported Agriculture (CSA) along with their structuration in AMAP' networks at different geographical levels. This PhD thesis suggests the existence of a tension between stability and representativeness under democratic settings and discusses, in different cases, the effect of several factors on this tension, like the number of topics discussed in the organization, the state of mind of members, the existence of structured communication, or the spatial repartition of members. In a second part, this Phd thesis deals with risk sharing groups between agents heterogenous in terms of risk exposures, as it is the case in AMAP between producers and consumers. It underlines how learning by agents of their risk exposures through times, which is equivalent here to constantly revise their preferences with respect to the characteristics of their organization, can stabilize risk-sharing groups mixing heterogenous agents and how this effect is strengthen by the introduction of other-regarding-preferences, like altruism or inequality aversion.