Conceptual system dynamics and agent-based modelling simulation of interorganisational fairness in food value chains: Research agenda and case studies

McGarraghy, Seán et al. Article — Published Version Conceptual system dynamics and agent-based modelling simulation of interorganisational fairness in food value chains: Research agenda and case studies Agriculture Provided in Cooperation with: Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle (Saale) Suggested Citation: McGarraghy, Seán et al. (2022) : Conceptual system dynamics and agent-based modelling simulation of interorganisational fairness in food value chains: Research agenda and case studies, Agriculture, ISSN 2077-0472, MDPI, Basel, Vol. 12, Iss. 2, https://doi.org/10.3390/agriculture12020280 This Version is available at: https://hdl.handle.net/10419/251273 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/   Citation: McGarraghy, S.; Olafsdottir, G.; Kazakov, R.; Huber, É.; Loveluck, W.; Gudbrandsdottir, I.Y.; ˇ Cechura, L.; Esposito, G.; Samoggia, A.; Aubert, P.-M.; et al. Conceptual System Dynamics and Agent-Based Modelling Simulation of Interorganisational Fairness in Food Value Chains: Research Agenda and Case Studies. Agriculture 2022,12, 280. https://doi.org/10.3390/ agriculture12020280 Academic Editors: Wojciech J. Florkowski, Francesco Caracciolo and Sanzidur Rahman Received: 30 November 2021 Accepted: 14 February 2022 Published: 16 February 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). agriculture Article Conceptual System Dynamics and Agent-Based Modelling Simulation of Interorganisational Fairness in Food Value Chains: Research Agenda and Case Studies Seán McGarraghy 1,* , Gudrun Olafsdottir 2, Rossen Kazakov 2,Élise Huber 3, William Loveluck 3, Ingunn Y. Gudbrandsdottir 2, Lukᚡ Cechura 4, Gianandrea Esposito 5, Antonella Samoggia 6, Pierre-Marie Aubert 3, David Barling 7, Ivan Ðuri´c 8, Tinoush J. Jaghdani 8, Maitri Thakur 9, Nína M. Saviolidis 2and Sigurdur G. Bogason 2 1School of Business, University College Dublin, D04 V1W8 Dublin, Ireland 2Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Dunhagi 5, 107 Reykjavik, Iceland; [email protected] (G.O.); [email protected] (R.K.); [email protected] (I.Y.G.); [email protected] (N.M.S.); [email protected] (S.G.B.) 3Institute for Sustainable Development and International Relations (IDDRI), 41 Rue Du Four, 75007 Paris, France; [email protected] (É.H.); w[email protected] (W.L.); [email protected]g (P.-M.A.) 4Department of Economics, Faculty of Economics and Management of the Czech University of Life Sciences Prague, Kamýcká129, 16500 Prague, Czech Republic; [email protected] 5ART:ER—Attractiveness Research Territory, Via P. Gobetti, 101, 40129 Bologna, Italy; [email protected] 6Department of Agriculture and Food Science, University of Bologna, Viale Fanin 50, 40125 Bologna, Italy; [email protected] 7Centre for Agriculture, Food and Environmental Management Research, University of Hertfordshire, College Lane, Hatfield, Herts AL10 9AB, UK; [email protected] 8Leibniz Institute for Agricultural Development in Transition Economies (IAMO), 06120 Halle, Germany; [email protected] (I.Ð.); [email protected] (T.J.J.) 9SINTEF Ocean, Postboks 4762, Torgard, N-7465 Trondheim, Norway; [email protected] *Correspondence: [email protected] Abstract: System dynamics and agent-based simulation modelling approaches have a potential as tools to evaluate the impact of policy related decision making in food value chains. The context is that a food value chain involves flows of multiple products, financial flows and decision making among the food value chain players. Each decision may be viewed from the level of independent actors, each with their own motivations and agenda, but responding to externalities and to the behaviours of other actors. The focus is to show how simulation modelling can be applied to problems such as fairness and power asymmetries in European food value chains by evaluating the outcome of interventions in terms of relevant operational indicators of interorganisational fairness (e.g., profit distribution, market power, bargaining power). The main concepts of system dynamics and agent-based modelling are introduced and the applicability of a hybrid of these methods to food value chains is justified. This approach is outlined as a research agenda, and it is demonstrated how cognitive maps can help in the initial conceptual model building when implemented for specific food value chains studied in the EU Horizon 2020 VALUMICS project. The French wheat to bread chain has many characteristics of food value chains in general and is applied as an example to formulate a model that can be extended to capture the functioning of European FVCs. This work is to be further progressed in a subsequent stream of research for the other food value chain case studies with different governance modes and market organisation, in particular, farmed salmon to fillet, dairy cows to milk and raw tomato to processed tomato. Keywords: food value chain; system dynamics; agent-based modelling; hybrid method; governance; interorganisational fairness Agriculture 2022,12, 280. https://doi.org/10.3390/agriculture12020280 https://www.mdpi.com/journal/agriculture Agriculture 2022,12, 280 2 of 30 1. Introduction A food system constitutes a series of actors performing activities and making decisions involved in bringing products from primary production, through processing and distribution to the final consumer [ 1 ]. It is generally acknowledged that industrialised food systems are neither sustainable nor resilient and a major transformation is needed [ 2 , 3 ]. Pressures to address and implement measures within Food Value Chains (FVCs) to increase the sustainability of food systems have been mounting in recent years [ 4 , 5 ]. The European Green Deal [ 6 ] and, in particular, the Farm to Fork strategy have been instrumental drivers of change for FVCs and have placed emphasis on sustainability and resilience as key priorities to address for European food systems [7]. Food system transformation highly depends on the collaboration and cooperation of FVC actors which is where the issue of fairness plays an important role since actors are less likely to collaborate and coordinate activities when they perceive themselves to be impacted by unfair trading practices (UTPs) [ 8 ]. Earlier findings have indicated that the negative impact of unfair trading practices on small and medium size enterprises (SMEs) in the EU food sector is affecting the competitiveness of the industry [ 9 ]. Another topic of concern is the effect of EU competition law on collaborative practices which has been identified to be a barrier to collaborative sustainability initiatives in food value chains [ 10 ]. The problem of interorganisational fairness in FVCs is associated with power asymmetries and fair value distribution among actors. In general, profit in a market driven system is a prerequisite for the continued operation of businesses and the price is one of the most important factors that will increase the fairness perceptions of FVC actors [ 11 ]. However, the short-term vision to generate profit may have detrimental impact on supply chain relations if power asymmetries in the supply chain undermine the operational profitability of smaller agents in the chain [ 11 , 12 ]. Bargaining power in interorganisational relationships is a consequence of both the relative strategic significance of the partners (i.e., size of supplier or buyer) and the availability of alternatives (i.e., number of available suppliers/buyers and ease of switching supplier/buyer) [ 13 , 14 ]. Bargaining power is considered relevant to capture the behaviour of complex modern food value chains which are characterised by strategic coordination and horizontal concentration in retail and food manufacturing [15,16]. The organisation of food value chains as part of the overall food system entails various governance forms and structural characteristics. Strategic coordination through mergers and acquisitions in food manufacturing and retail and the formation of horizontal alliances has shifted the balance of power in food value chains [ 12 , 15 , 17 , 18 ]. The resulting weak position of farmers, in particular agricultural producers, has been of concern as they may be placed under pressure and have limited bargaining power in negotiations with larger buyers such as food manufactures and retail [ 19 – 21 ]. In response to this concern, the EU Directive (2019/633) on unfair trading practices (UTP) aims at protecting weaker suppliers, primarily farmers, including their organisations (e.g., cooperatives) against their buyers, as well as suppliers of agri-food products which are further downstream [22]. In an effort to assess the influence of policy measures to enhance fairness in FVCs, simulation modelling is of interest. Fairness is a complex issue and often defined along two main dimensions as distributive fairness and procedural fairness. Procedural fairness concerns the procedures leading to outcomes. The outcomes can be perceived as fair or unfair (i.e., distributive fairness) but the procedures leading to these outcomes can themselves also be considered fair or unfair (i.e., procedural fairness). The procedures in place certainly affect outcomes, although a high level of procedural fairness does not automatically lead to high levels of distributive fairness. Stakeholders’ views on fairness are focused on price setting and how pricing decisions are made. The perception of fairness is often subjective and highly influenced by where in the supply chain the actors are embedded [ 8 , 23 ]. Various factors can influence the outcome, such as different firm strategies related to, e.g., transaction costs, capacities, collaboration, entry barriers or equal power among partners to define prices, access to relevant information and the treatment Agriculture 2022,12, 280 3 of 30 of different partners in a supply chain on behalf of a powerful party [ 11 , 12 ]. Furthermore, the link between supplier fairness and relationship quality has been emphasised; and supplier dependence is another important factor that can affect the trust between suppliers and retailers [ 24 ]. The uncertainty of prices linked to the volatility of commodity prices and various requirements set by the more powerful actor in buyer and supplier relations, including sustainability criteria, needs to be considered when assessing the outcome of price negotiations and distributive fairness. With a focus on interfirm relations and modelling interorganisational fairness, quantitative indicators must take into account bargaining power and the importance of price for FVC agents, in their effort to maximise their profit or utility. Social concepts more qualitative in nature are excluded from the scope of this study. This paper builds on the conceptualisation and operationalisation of interorganisational fairness in [ 25 ] where indicators for use in a quantitative model were proposed, e.g., profit margin as an indicator of distributive fairness and indicators for procedural fairness related to market power and bargaining power [ 25 ]. The product flows can be simulated using system dynamics (SD) which is well suited for modelling such flows [ 26 – 28 ]. The decisions controlling the product flow and pricing, which are the principal part of the model, can be modelled using agent-based modelling (ABM). The main advantage of ABM is its ability to model social interactions and so it can aid the study of subjects such as cooperation, competition, and collaboration in supply chains [ 29 ]. The aim is not to determine an absolute measure of fairness using these indicators, but rather to ascertain transitions towards fairer outcomes. This approach is in keeping with the European Parliament’s depiction, which, rather than providing a strict value measure of UTPs, emphasises the presence of gross deviations away from good commercial conduct. There seems to be a shortage in the literature of research that considers hybrid system dynamics & agent-based models of the whole food supply chain, from producers to consumers, thereby incorporating the full extent of interaction and feedback within the chain; one contribution of this paper is to address this gap in the literature. The main research question to be addressed here is: Can simulation of food value chains be used to assess: (a) the effect of strategic interventions on power structure; and (b) the impact on price negotiations between actors in food value chains; where distributive fairness is assessed by profit margin received by value chain actors and power structure is assessed by a proxy for market power and bargaining power? Hence, the objective of this paper is threefold: • To set an agenda for research into simulation models of FVCs which hybridise system dynamics (SD) and agent-based modelling (ABM) methods, and to communicate the benefits of this approach to an audience of experts in agriculture and FVCs. • To present how interorganisational fairness is related to governance and power asymmetries and thus identify potential sources of unfair marketing condition. This characterises the “real world problem” to be addressed by the simulation model. • To select the most important elements of the real-world problem using methods such as cognitive maps and so derive a conceptual model to address the interorganisational fairness problem. The structure of the rest of this paper is as follows. Section 2introduces the main concepts of SD and ABM, and the use of a hybrid model is proposed and argued for the applicability to FVCs. Section 3provides the background of the study including an overview of the food system structure and aspects of the fairness problem and governance in FVC case studies. In Section 4the methodologies applied for the conceptualisation of the model are detailed. The results in Section 5present an outline on how this approach is conceptualised for a particular FVC case study by use of cognitive maps for the wheat to bread chain and we show at a high level how a conceptual hybrid of SD and ABM can model FVCs and the problem of fairness. Section 6is discussion and Section 7gives conclusions. Agriculture 2022,12, 280 4 of 30 2. Simulation Modelling Background For any business operating in a dynamic environment, such as agri-food businesses, optimisation and exact solution techniques cannot give a full and realistic picture of the business for a number of reasons, not least the underlying variability and complexity of the environment [ 30 ]. In such complex applications, simulation approaches can give an understanding of how factors such as labour variability, shortages or obsolescence of materials, etc. can affect overall performance [ 30 ]. Such simulation models are extensively used in the literature [ 31 – 33 ] and specifically in the agri-food domain [ 29 ]. Analytical closed-form solutions for multitier supply and value chains exist only in very simple cases, e.g., a two-tier supply chain; in other cases, the only practical approach to modelling is to simulate supply and value chains and so analyse the flow of money, information and material through the chain. There are several simulation approaches [ 33 ], including system dynamics, agent-based modelling and discrete-event simulation. In this section, we give an overview of the main simulation modelling approaches extant and the reasoning for our choice of a hybrid approach involving system dynamics and agent-based modelling: namely, that distributive and procedural interorganisational fairness can be captured by the flows of money and information (distributive) and the relations between interacting agents (procedural). The endogenous and exogenous characteristics of organisational and market complexity are a source of causal ambiguity, emergent behaviour and self-organisational dynamics [ 34 ]; understanding of these is enhanced by general systems theory [ 35 , 36 ] and the system dynamics field of research [ 27 , 28 , 37 – 39 ]. System dynamics (SD) is an approach to understanding the nonlinear behaviour of complex systems over time using stocks, flows, internal feedback loops, functions and time delays. It is a mathematical modelling technique and methodology to frame, understand, and discuss complex issues and problems. Its basis is the recognition that the structure of a system—the many circular, interacting, sometimes time-delayed relationships among its components—can be just as important in determining its behaviour as the individual components themselves. There have been some applications in agriculture, e.g., [ 40 ] and food supply chains using system dynamics [ 41 – 44 ]. However, earlier studies rarely included the flow of money through the system; and, even when included its impact on decision making and the dynamics of the system were usually neglected. Financial factors greatly impact on decision making and therefore the physical flow of products and services; hence, our focus on value chains. A more contemporary method for exploring complex adaptive systems is agent-based modelling and simulation (ABM or ABS) which, in contrast to the system dynamics topdown “macroscopic” perspective, takes a bottom-up “microscopic” view for explaining agent-specific emergent system behaviour (Figure 1). ABM has several features that make it an appropriate approach for modelling complex characteristics of value chains such as actor behaviours and interactions [ 45 , 46 ]. An agent is a model of a real-world actor. Agents in such systems act in parallel within an environment, interacting and competing for control over resources in an adaptive manner, subject to a condition/action rule pattern connected to a specific behavioural decision-making structure [ 47 – 50 ]. Agents may be intelligent, e.g., using ordering rules evolved by learning algorithms [ 51 ]. The environment represents all real-world factors not represented by agents. Agent-based simulation models are typically built from the bottom up by identifying agents in the system and defining their behaviours, including how they interact with other agents and their environment. The behaviour of the system as a whole emerges out of multiple concurrent individual agent behaviours. Each agent is: self-contained (an identifiable, discrete individual with a set of characteristics or attributes, behaviours, and decision-making capability [ 49 ]); autonomous (controls its internal state and its own behaviour); situated (in the environment); and social (interact with other agents). Agents have: •attributes such as capacity, number of employees, production level; • behaviours: the agent senses the environment, decides and acts (within constraints): responds to actions of other agents, regulations, flows of goods, money, information; Agriculture 2022,12, 280 5 of 30 •goals that drive its behaviour (maybe to optimise, maybe to satisfy requirements); •memory and the ability to learn and/or adapt based on experience; •(possibly) resources or stocks. Agriculture 2022, 12, x FOR PEER REVIEW 5 of 31 • attributes such as capacity, number of employees, production level; • behaviours: the agent senses the environment, decides and acts (within constraints): responds to actions of other agents, regulations, flows of goods, money, information; • goals that drive its behaviour (maybe to optimise, maybe to satisfy requirements); • memory and the ability to learn and/or adapt based on experience; • (possibly) resources or stocks. Agent-based modelling, compared to traditional approaches to modelling economic systems, can be a more viable approach when there are reasons to think in terms of agents; for example, when the problem or research question to be explored is naturally represented by a large number of actors whose decisions and behaviours can be well-defined, which adapt and change, which learn, which engage in dynamic strategic interactions and relationships with other actors, and which can have spatial or temporal components to their behaviours and interactions [52]. An ABM can capture the structure of a system which has endogenously emerging mechanisms affecting its future evolution. In the food value chain context, an agent could be a consumer, retailer, producer or indeed any chain actor: these are independent actors, each with its own motivations and agenda, but each influenced by the environment. Discrete event simulation (DES) is a methodology for modelling the behaviour of a complex real-world system comprising a number of separate processes where stochastic variability is an important consideration. A DES models the real system as discrete entities (individual items, e.g., a FVC order) which move through a network of queues (places where entities wait for processing) and activities (processing of entities, e.g., food processing or packing). Thus, each process consists of a discrete time-ordered sequence of events (discrete changes in system state) considered important by the modeller, and each event occurs at a particular time (timestamp); for example, if modelling a bakery, the events studied could be baking and packing events. Time moves forward in discrete steps (the moments at which events occur), and in a general DES, not all time steps need have the same duration. Standard applications of DES include modelling of manufacturing systems and queuing systems with stochastic aspects. As this paper does not consider queueing in the system, but only uses a single system clock, the model developed has only minor elements of a DES; however, this description is included for completeness. For further details, see [33] and the references therein. Hybrid simulation comprising ABM to capture the autonomous and interacting decision making behaviour of the supply chain actors, together with DES, has been used to model the production processes within a food value chain [53]. Figure 1 illustrates the relative levels of abstraction of SD, DES and ABS. Figure 1. Level of abstraction of different simulation approaches. Figure 1. Level of abstraction of different simulation approaches. Agent-based modelling, compared to traditional approaches to modelling economic systems, can be a more viable approach when there are reasons to think in terms of agents; for example, when the problem or research question to be explored is naturally represented by a large number of actors whose decisions and behaviours can be well-defined, which adapt and change, which learn, which engage in dynamic strategic interactions and relationships with other actors, and which can have spatial or temporal components to their behaviours and interactions [ 52 ]. An ABM can capture the structure of a system which has endogenously emerging mechanisms affecting its future evolution. In the food value chain context, an agent could be a consumer, retailer, producer or indeed any chain actor: these are independent actors, each with its own motivations and agenda, but each influenced by the environment. Discrete event simulation (DES) is a methodology for modelling the behaviour of a complex real-world system comprising a number of separate processes where stochastic variability is an important consideration. A DES models the real system as discrete entities (individual items, e.g., a FVC order) which move through a network of queues (places where entities wait for processing) and activities (processing of entities, e.g., food processing or packing). Thus, each process consists of a discrete time-ordered sequence of events (discrete changes in system state) considered important by the modeller, and each event occurs at a particular time (timestamp); for example, if modelling a bakery, the events studied could be baking and packing events. Time moves forward in discrete steps (the moments at which events occur), and in a general DES, not all time steps need have the same duration. Standard applications of DES include modelling of manufacturing systems and queuing systems with stochastic aspects. As this paper does not consider queueing in the system, but only uses a single system clock, the model developed has only minor elements of a DES; however, this description is included for completeness. For further details, see [ 33 ] and the references therein. Hybrid simulation comprising ABM to capture the autonomous and interacting decision making behaviour of the supply chain actors, together with DES, has been used to model the production processes within a food value chain [53]. Figure 1illustrates the relative levels of abstraction of SD, DES and ABS. In the process of developing a simulation model to capture behaviour of actors in a food system, a crucial stage is the initial conceptualisation. This involves analysis of the underlying feedback structure of the system to formulate hypotheses about the system’s dynamic behaviour. A system dynamics modelling approach is useful for studying changes over time in complex supply systems with the aim of building both the understanding of Agriculture 2022,12, 280 6 of 30 complexity needed to find effective policies, and the confidence to use that understanding to take action [28]. A food supply system can be viewed as integrated downstream physical flows, upstream financial flows and decision chains that link these flows. Central to this idea is that supply systems are driven by profit and regulated by market dynamics [ 54 ]. Using this approach, the qualitative conceptual VALUMICS model of the macroscopic structure of a generic food system was initially presented as a simplified causal loop diagram (CLD) based on supply, demand, and price. Each step in the supply chain (e.g., farming, processing, and retail) was captured as a part of a food supply chain feedback structure describing the relationship between a supplier and a customer: it is natural to model these as agents. Therefore, the chain of agents, each aiming at maximising profit and minimising cost, is in aggregate a reinforcing supply system. Studying the structure and dynamics of food chain systems in VALUMICS as integrated supply-, valueand decision chains underscores the complexity of such systems [ 54 ]. Additionally, to use a model of a supply system to foresee policy implications, it is useful to consider not only the physical flow of products in the system but also the associated flow of funds and the effect it has on decision making. The dynamics of the systems being studied, with continuous and discrete elements, as well as the heterogeneity of the actors within them, motivates us to investigate a hybrid simulation methodology that can capture both system flows and actor behaviours in food value chains. The product flows can be simulated using SD, which is well suited for modelling such flows. The decisions controlling the product flow and pricing, which are the principal part of the model, were modelled using ABM. Since time passes in discrete steps, the model is also a DES. Hybrid models are of growing importance in operational research [ 33 ] but seem to be less commonly used in the agri-food context, though there has been some recent work on short supply chains [55]. A major aim of the VALUMICS project was to develop an integrated hybrid SD/ABM quantitative simulation model for use by policy makers and other stakeholders, and so contribute both academically and practically. The model developed is regarded as a laboratory for experiments and simulations to explore if and how regulatory interventions and changes in individual actor behaviour may drive overall system behaviour. It focusses on fairness issues, especially unfair trading practices in food value chains, and underpins other VALUMICS work on scenario development with a broader remit (e.g., resilience, sustainability, and integrity in general). The food systems studied comprise a large and complex system as discussed in Section 3: these can only be interpreted from a limited viewpoint both because of the modellers’ need to select and simplify, and the lack of availability of complete and exhaustive information on the system: thus, many simplifications and assumptions are needed. This paper derives a conceptual model giving a functional specification: what the hybrid simulation model will do. It highlights the main relevant decisions of each market actor in each FVC, and their explanations based on procedural and other aspects, including conditional factors and what-if questions. The conceptual modelling is addressed using qualitative techniques such as cognitive maps, agent interaction and agent behaviour rule maps. These also aid the modeller in reframing the main research question in terms of the model, exploring the model boundary, and generating dynamic hypotheses for simulation testing. The description of this conceptualisation stage is exemplified only by the French wheat to bread case study, which is the focus of the modelling work presented in this paper. Other case studies investigated are presented in the following section to provide an overview of real-world issues concerning fairness in FVCs. All modelling approaches assume certain properties of the real-world situation being modelled, that is, represent it in a simplified way for reasons of economy of description, mathematical tractability or computational cost). The hybrid SD/ABM approach can capture flows of money, materiel and information in the FVC (SD) and choices/decisions/actions of individual actors based on what they see around them (ABM). The level of detail of the model constrains what can be studied and so what questions can be addressed. Computa- Agriculture 2022,12, 280 7 of 30 tional cost considerations and available hardware constrain the number of agents modelled to a fraction of the number of real-world actors (though careful adherence to real-world proportions may mitigate this provided the numbers of agents can give statistically significant conclusions). Furthermore, we as modellers select for inclusion in the model what we regard as important; and we may potentially exclude important aspects of the real world from the model. In this case, the agent attributes and behaviours chosen are those relevant to interorganisational fairness as measured by our chosen metrics; attributes and behaviours not connected to this (e.g., to do with cultural aspects or sustainability) have not been captured and so questions involving those attributes and behaviours cannot be answered by this paper’s approach. For example, such important real-world concepts as community resilience, long-term sustainability, and cultural diversity have been excluded from the scope of the model. The attributes and behaviours could also be extended to address other questions, which would make the model more complex and indeed cumbersome, if not computationally intractable. By the same token, other simpler approaches (e.g., a pure SD model with no modelling of actors as agents) could address some questions—if to a lesser extent of richness—at a lower computational cost. All models are simplifications but can be useful when populated with reliable data. Conversely, data gaps can limit the quality of a model. Lack of data turned out to be a major limiting factor in the later building of this model, and high-impact assumptions needed to be made when data gaps were encountered, especially concerning firm level data and particularly for non-farmer actors. Secondary data from different European level data, national level data and regional level data was available on e.g., Eurostat, FAOSTAT, Euromonitor databases and national databases, while FADN and Amadeus databases were used for micro data respectively at farm and company level: • Secondary data from different databases have different structures caused by divergent product classifications, time periods covered, commodity aggregations, and geographical reach. At the farm level the FADN data was detailed and possible to reconstruct to meet the modelling and analysis needs. However, at the processing industry level, the data available does not provide physical volumes passing through the processing industries; thus, it is difficult to link biophysical flows and socio-economic outcomes. • Other limitations constraining the research and development of the model were the facts that data is aggregated at the national level and that no data is available regarding the share of differentiated vs. commodified/standardised production. • Data is available at the firm level for specific firms; however, it is often incomplete (e.g., few data on business expenses) and big firms are often over-represented in the sample. • Another factor causing difficulties is that firms are classified based on their sector of activity. For firms operating in more than one sector, all data values are assigned to the dominant sector. Also, some of the food processing is also realised by retailers (e.g., cutting and packing meat) so it is difficult to separate their main business from the processing activities. 3. Food System Background—Case Studies In a conceptual modelling exercise, it is crucial to examine as much of the context of the problem as possible to avoid the possibility of omitting some vital concept or data. This is done here, to convey the richness of the real-world system being modelled and the compromises necessary to arrive at a simplified yet requisite model. This research is part of the EU H2020 VALUMICS project which aims at improved understanding of the dynamics of food value chains. The general objective is to provide tools and approaches to enable decision makers to evaluate the impact of strategic and operational policies aimed at enhancing fairness, integrity, and resilience in future scenarios of sustainable food value chains (FVCs). The VALUMICS project applied a systems approach and involved an interdisciplinary group of experts to perform analysis of food system related issues. This provided insights on the structure, material flows, governance, economics, environmental Agriculture 2022,12, 280 8 of 30 impacts, and consumer behaviour related to different food value chain [ 56 , 57 ]. The research activities were carried out through the following case studies: Dairy cows to milk, Beef cattle to steak, Wheat to bread, Farmed salmon to fillets and Tomato to processed tomato. The FVCs all have a similar structure as depicted in Figure 2, showing the integration of flow charts of the selected case studies with the flow of products through similar stages of the supply chains. Agriculture 2022, 12, x FOR PEER REVIEW 8 of 31 compromises necessary to arrive at a simplified yet requisite model. This research is part of the EU H2020 VALUMICS project which aims at improved understanding of the dynamics of food value chains. The general objective is to provide tools and approaches to enable decision makers to evaluate the impact of strategic and operational policies aimed at enhancing fairness, integrity, and resilience in future scenarios of sustainable food value chains (FVCs). The VALUMICS project applied a systems approach and involved an interdisciplinary group of experts to perform analysis of food system related issues. This provided insights on the structure, material flows, governance, economics, environmental impacts, and consumer behaviour related to different food value chain [56,57]. The research activities were carried out through the following case studies: Dairy cows to milk, Beef cattle to steak, Wheat to bread, Farmed salmon to fillets and Tomato to processed tomato. The FVCs all have a similar structure as depicted in Figure 2, showing the integration of flow charts of the selected case studies with the flow of products through similar stages of the supply chains. Figure 2. An overview of the food system approach demonstrating the key stages in food value chains and the associated flow of stocks for the food supply chains case studies in the VALUMICS project (adapted from [57]). It is generally understood that primary production, in particular intensive farming, cause the main environmental burden in the whole life cycle of agri-food, including meat, dairy, and aquaculture products, where the use of feed connects the challenges of the food supply chains (Figure 2). The end market of products also matters, since the use of fuel during transport can also contribute considerable climate change impacts of exported products in distant markets and the waste generated throughout all stages [58,59]. Although most food value chains share many sustainability challenges, they differ in several aspects affecting the prevalence and extent of interorganisational fairness problems associated with profit distribution which is the focus of this paper and the scope of the simulation model described herein. Differences regarding interorganisational fairness can, to some extent, be attributed to the governance and the strategic coordination in terms of horizontal or vertical collaboration, and the rules for distributing value added [60–62]. This was demonstrated in the governance analysis in case studies in the VALUMICS project [8]. This section provides a summary of the main findings of governance analysis in terms of interfirm relations in four case study FVCs. Moreover, economic analysis in the selected case studies provide evidence to substantiate the findings. These include studies on market power, price formation and price transmission [63], assessment of economies of scale and technical efficiency [64], persistence of supply chain relations [65], including also statistical analysis of agribusiness profitability [66]. The economic analysis provided Figure 2. An overview of the food system approach demonstrating the key stages in food value chains and the associated flow of stocks for the food supply chains case studies in the VALUMICS project (adapted from [57]). It is generally understood that primary production, in particular intensive farming, cause the main environmental burden in the whole life cycle of agri-food, including meat, dairy, and aquaculture products, where the use of feed connects the challenges of the food supply chains (Figure 2). The end market of products also matters, since the use of fuel during transport can also contribute considerable climate change impacts of exported products in distant markets and the waste generated throughout all stages [58,59]. Although most food value chains share many sustainability challenges, they differ in several aspects affecting the prevalence and extent of interorganisational fairness problems associated with profit distribution which is the focus of this paper and the scope of the simulation model described herein. Differences regarding interorganisational fairness can, to some extent, be attributed to the governance and the strategic coordination in terms of horizontal or vertical collaboration, and the rules for distributing value added [ 60 – 62 ]. This was demonstrated in the governance analysis in case studies in the VALUMICS project [ 8 ]. This section provides a summary of the main findings of governance analysis in terms of interfirm relations in four case study FVCs. Moreover, economic analysis in the selected case studies provide evidence to substantiate the findings. These include studies on market power, price formation and price transmission [ 63 ], assessment of economies of scale and technical efficiency [ 64 ], persistence of supply chain relations [ 65 ], including also statistical analysis of agribusiness profitability [ 66 ]. The economic analysis provided useful insights to the functioning of FVCs [ 67 ] and further underpinned the conceptual modelling work described in Sections 4and 5. Considering the socio-economic impacts, the profitability and competitiveness of the enterprises constituting food value chains are key elements to ensure employment and livelihoods. A snapshot of the key results of the governance analysis (Figure 3) shows the most typical governance modes in these FVCs and, thus, possible sources of unfair market conditions. There is complexity in the governance modes, the size of firms has an impact, and various externalities have shaped the food value chains and motivated changes in the Agriculture 2022,12, 280 15 of 30 4.5. Stages of Conceptual Modelling: Cognitive Maps and Related Techniques The modelling effort applies techniques including cognitive mapping and agent behaviour mapping for system analysis and agent rules definition, to determine the model content including the physical and social elements of the system and the links between them. The VALUMICS conceptual model derived is described in the Results section. The goal of this qualitative agent modelling approach is to support the conceptual, functional, and technical specification for the quantitative modelling phase. The behaviour of each agent can be captured in a story/narrative which explains which agent does what with whom and when. This requires detailed input from the subject matter experts of the case study teams, to define agents and their decisions, behaviours, and interactions. A useful high-level approach to this is to consider what actors there are in a particular value chain and, for each one, think of how that actor (modelled as an agent) will behave in practice: •What happens in “a day in the life” of this actor—what does this actor do? • What interactions has this actor with other actors, whether in the same tier or not (which agent rules affect which other agent rules)? •What interactions has this actor with the environment? •What decisions does this actor make (related to fairness, e.g., pricing decisions)? •What influences these decisions (prices, regulations, environmental factors)? Cognitive maps are used to capture what system element influences what, and decision tables are used to identify the agent decisions together with what influences them and their other characteristics. In the VALUMICS hybrid model the behaviour of agents was defined in terms of decision rules executed upon special events and in interactions with other agents. Cognitive mapping [ 87 , 88 ] is related to mapping individual and group mental models about a research question and to Cognition theory [ 81 , 82 ]. For example, Elsawah et al. [ 83 ] use cognitive mapping to capture and analyse qualitative information from stakeholders on the issue of viticulture irrigation in Australia, and further to inform a better approach for capturing agents’ decision-making procedures. They demonstrate how cognitive mapping brings advantages for ABM design and parameterisation: ‘The action-oriented nature of concepts in the map makes it explicit about “what action is taken”, and “by whom”. Therefore, the structure and flow of decision making becomes explicitly represented in a cognitive map. Thus, the cognitive map allows for capturing behaviour rather than just attributes of agents, as well as the interactions between actors’ perceptions, states of their worlds, and choice states’ [83]. Agent interaction mapping (AIM) and Agent behaviour mapping (ABM) are techniques, associated with the cognitive mapping technique but with a different aim, to analyse agents, agents’ rules and interrelations [ 89 ]. The design purpose and theoretical framework of the cognitive and agent mapping techniques are presented in Table 1. The results of the cognitive mapping are presented in detail in this paper using as an example the wheat to bread case study (Section 5.3). Table 1. Cognitive and agent mapping techniques. Mapping Technique Design Purpose Theory Cognitive Map (CoM) Mapping key market resources, agents, influencing factors and variables and eliciting feedback interrelations; Analysis of market structure and feedback dynamic; Cognitive mapping theory [81,82] RDT [90,91] Agriculture 2022,12, 280 16 of 30 Table 1. Cont. Mapping Technique Design Purpose Theory Agent Interaction Map (AIM) Analysis of agents’ interactions and influencing dynamics; Mapping agent interactions including identifying each agent’s key behavioural decision rules and key influencing factors; BDT [92–94] Stakeholders Management mapping concept [87] Agent Behaviour Map (ABM) Mapping each agent’s behavioural decision rule in more detail through an agent behavioural map; Analysis of agent decision rules and behaviour; BDT [92,95] 5. Results In a conceptual modelling exercise, the “result” is the model derived. Initial work to lay the foundation for the modelling is to provide a depiction of the FVC structure. This is carried out in conjunction with subject matter experts. It entails (1) identifying the different tiers of agents which play a role in the supply chain, as well as (2) determining relevant categories of agents within each tier. Most food supply chains have the same basic structure (i.e., producers, collectors, processors, retailers, consumers). However, some of those categories may merge or, conversely, be divided into several tiers depending on the supply chain. Notwithstanding such variations among specific FVCs, we may reiterate that most FVCs have the same basic structure. This allows us to create a general hybrid SD/ABM simulation modelling architecture (prototype conceptualisation) applicable for all FVCs being studied. All the main system components relevant to the research focus (problem definition and boundary selection) are taken into consideration, including their interrelations (between financial resources and production resources and between agents and agents, and agents and resources). For each case study, the physical and social elements of the food system are listed up. These elements can be agents (capable of independent decision making) or stocks, flows or controls (the SD elements). Having identified these elements, the problem definition then leads to “what-if” questions. 5.1. What-If Questions “What-if” questions capture the hypotheses the model is intended to address and test. Here, the range of such questions for the VALUMICS case studies is discussed. Procedural fairness can be addressed by the model as experimental factors using whatif questions. Such questions can address different procedural fairness issues, including unequal power among partners to define prices and unequal access to information resulting from the way in which the individual FVC is governed (e.g., producer organisations and price auctions). Examples of procedural fairness what-if questions to be addressed include the lists of prohibited practices in business-to-business relationships according to the Directive on UTPs. These include customs which may be considered of a procedural nature, e.g., relating to payment terms (such as late payments) and other elements of contractual relations (such as short-notice cancellations). Further questions of interest to explore within the model include aspects of the link between the degree of market/bargaining power of FVC actors and the gross margin profit in terms of fair value distribution by exploring the impact of financial markets, reference price negotiation etc. By observing to what extent gross margins vary in different what-if experiments, it may be possible to indicate when the FVC is becoming fairer. Agriculture 2022,12, 280 17 of 30 In general, what-if questions may be categorised as follows: 1. Changes in macroscopic environment parameter values (a) Choices for production, consumption and other environment parameter values that will affect the macro-operation of the simulation model run, as they will prescribe values for supply and demand, e.g.,: • What happens to the FVC when levels of production/consumption change? • How dochanges insupply/demandaffectagents: dosomeagents cease trading? •What is the effect on fairness of these changes? (b) Hypothetical interventions by introducing regulatory, competition structure, and technological related changes which have the purpose of improving fairness in the simulated FVC systems, e.g.,: •What if the level of CAP subsidies was changed? •What if the CAP subsidy were removed? •What if price controls came in? 2. Micro-level what-if questions that determine or affect the behaviour of an individual actor/agent’s decision making. For example, how easy is it to switch partners? These are elucidated by exploring that agent’s linkages in the cognitive map for the case study. 5.2. The French Wheat to Bread Case Study Figure 5, derived from the joint modelling workshops described in Section 4.2, gives more detail on the separation of the French wheat to bread FVC into tiers and sizes of actors than presented earlier in Figure 2. In the wheat to bread supply chain, the “processing” stage is divided between the collectors (who collect and store wheat) and the millers (who transform the wheat into flour). Key actors are large millers, who are mostly large producerowned cooperatives and control supply of flour mixes to traditional bakeries, putting bakeries in a relatively captive relationship as discussed before. The consumption of bread from traditional bakeries currently represents around 50% of national bread consumption in France [8]. Agriculture 2022, 12, x FOR PEER REVIEW 18 of 31 Figure 5. French wheat to bread FVC agents to be modelled as part of the ABM simulation modelling conceptualisation, with the highest priority agents indicated by a red dashed line. The agent-based model must distinguish among different “types” of actors within each tier (Section 4.4). Capturing this diversity of actors is crucial to the ABM as it allows the factoring in of the diversity of responses/strategies that agents might have in the supply chain and therefore how the supply chain might evolve in different scenarios. The main question then is how or on what basis to differentiate actors within each tier. Agents can be categorised according to different criteria e.g., size of the agent—which can reflect the number of employees, or the production volume, or value of production, etc.—type of production system, type of business model, etc. For some tiers in the supply chain, the categorisation may be relatively straightforward. For example, this is the case for the wheat to bread FVC retailers, which can be clearly divided into traditional bakers, industrial bakers and in-store bakers: these have very different business models, economic productivity levels, etc. The topic of categorising certain specific actors (e.g., industrial bakers) according to size (e.g., medium and large) is a question in itself. In other less straightforward cases, the objective of the model, i.e., what output variables are most important to the model, can drive out relevant agent distinctions. For the VALUMICS wheat to bread model, the key output variables include the number of workers, size of agents and value added. It is thus pertinent to make a distinction between agents characterised by very different levels of workers employed or productivity, as opposed to different levels of value-added or revenue (a variable which is potentially connected to the type of outlet the agents mainly have). For example, small millers have a lower material productivity than big millers but mainly sell their flour to traditional bakeries, at higher prices than those attained by an industrial bakery; while medium and large millers have a higher material productivity but sell a high share of their flour at lower prices to industrial bakers. In addition, the distinction between FVC agents may be more generally bounded by constraints such as data availability. When looking for data to characterise the different agents, it may be that the available data only allows the distinguishing of actors in a very limited way (e.g., by size). One may thus choose to differentiate agents from a qualitative perspective (for example, based on the nature—private or cooperative—of the agent) rather than from a quantitative perspective. Such a distinction derived from more qualitative aspects must be based on the fact that these types of actors behave differently when facing a certain type of constraint. Figure 5. French wheat to bread FVC agents to be modelled as part of the ABM simulation modelling conceptualisation, with the highest priority agents indicated by a red dashed line. The agent-based model must distinguish among different “types” of actors within each tier (Section 4.4). Capturing this diversity of actors is crucial to the ABM as it allows Agriculture 2022,12, 280 18 of 30 the factoring in of the diversity of responses/strategies that agents might have in the supply chain and therefore how the supply chain might evolve in different scenarios. The main question then is how or on what basis to differentiate actors within each tier. Agents can be categorised according to different criteria e.g., size of the agent—which can reflect the number of employees, or the production volume, or value of production, etc.—type of production system, type of business model, etc. For some tiers in the supply chain, the categorisation may be relatively straightforward. For example, this is the case for the wheat to bread FVC retailers, which can be clearly divided into traditional bakers, industrial bakers and in-store bakers: these have very different business models, economic productivity levels, etc. The topic of categorising certain specific actors (e.g., industrial bakers) according to size (e.g., medium and large) is a question in itself. In other less straightforward cases, the objective of the model, i.e., what output variables are most important to the model, can drive out relevant agent distinctions. For the VALUMICS wheat to bread model, the key output variables include the number of workers, size of agents and value added. It is thus pertinent to make a distinction between agents characterised by very different levels of workers employed or productivity, as opposed to different levels of value-added or revenue (a variable which is potentially connected to the type of outlet the agents mainly have). For example, small millers have a lower material productivity than big millers but mainly sell their flour to traditional bakeries, at higher prices than those attained by an industrial bakery; while medium and large millers have a higher material productivity but sell a high share of their flour at lower prices to industrial bakers. In addition, the distinction between FVC agents may be more generally bounded by constraints such as data availability. When looking for data to characterise the different agents, it may be that the available data only allows the distinguishing of actors in a very limited way (e.g., by size). One may thus choose to differentiate agents from a qualitative perspective (for example, based on the nature—private or cooperative—of the agent) rather than from a quantitative perspective. Such a distinction derived from more qualitative aspects must be based on the fact that these types of actors behave differently when facing a certain type of constraint. It is also important to determine the unit/scale of the agent which is considered in the model. For example, processors may be considered at the processing unit level or at the company level. It is important to make an informed decision about whether to consider one or the other in the model. Again, one choice may be favoured by the data to hand. The case of retailers is a good example: the number of units might be high, but the number of groups is usually low (less than ten in most EU countries). In a model concerned with fairness, that considers the equity of value distribution, it may be more relevant to work at the group level, since even if different units are part of the group, only a few (or sometimes even one) central purchasing departments manage purchases for all units. This initial modelling step starts the process of collecting quantitative data on the FVC, e.g., number of actors in each tier, type of actor, volumes handled, number of workers, productivity, etc. This data then feeds into the next quantitative formalisation step. 5.3. Cognitive Map for the French Wheat to Bread Chain A cognitive map seeks to capture and display links or relationships among the previously listed elements of the FVC. The group modelling sessions mentioned in the previous Section 4.2 led to the cognitive map for price negotiation in the French wheat to bread case study food value chain shown in Figure 6and subsequent explanatory notes. The content of this map is now explained in more detail. All wheat to bread value chain actors depend on the international market because: (a) there are low trade barriers for imported wheat; (b) there are changing markets with different requirements; and (c) the domestic market is saturated. Agriculture 2022,12, 280 19 of 30 Agriculture 2022, 12, x FOR PEER REVIEW 19 of 31 It is also important to determine the unit/scale of the agent which is considered in the model. For example, processors may be considered at the processing unit level or at the company level. It is important to make an informed decision about whether to consider one or the other in the model. Again, one choice may be favoured by the data to hand. The case of retailers is a good example: the number of units might be high, but the number of groups is usually low (less than ten in most EU countries). In a model concerned with fairness, that considers the equity of value distribution, it may be more relevant to work at the group level, since even if different units are part of the group, only a few (or sometimes even one) central purchasing departments manage purchases for all units. This initial modelling step starts the process of collecting quantitative data on the FVC, e.g., number of actors in each tier, type of actor, volumes handled, number of workers, productivity, etc. This data then feeds into the next quantitative formalisation step. 5.3. Cognitive Map for the French Wheat to Bread Chain A cognitive map seeks to capture and display links or relationships among the previously listed elements of the FVC. The group modelling sessions mentioned in the previous Section 4.2 led to the cognitive map for price negotiation in the French wheat to bread case study food value chain shown in Figure 6 and subsequent explanatory notes. Figure 6. Example of cognitive map for price negotiations in the French wheat to bread FVC (Authors’ own conceptualisation first presented at a conference [96]) Note: Value chain actors and certain regulations/directives are shown in boldface; External (environment) variables affecting wheat volume/quality are in boxes; External (environment) economic variables are in boldface red; Supplies of wheat are shown in boldface italic; Decisions involving negotiation and/or calculation are shown in upright red; Other factors are shown in upright or italic font. The content of this map is now explained in more detail. All wheat to bread value chain actors depend on the international market because: (a) there are low trade barriers Figure 6. Example of cognitive map for price negotiations in the French wheat to bread FVC (Authors’ own conceptualisation first presented at a conference [ 96 ]) Note: Value chain actors and certain regulations/directives are shown in boldface; External (environment) variables affecting wheat volume/quality are in boxes; External (environment) economic variables are in boldface red; Supplies of wheat are shown in boldface italic; Decisions involving negotiation and/or calculation are shown in upright red; Other factors are shown in upright or italic font. 5.3.1. Factors Influencing the Price Negotiation for Wheat between Farmers and Collectors Collectors may be either cooperatives, which control 70% of the wheat market, or wholesalers, which control 30% [ 8 ]. Producers tend to be captive suppliers towards cooperatives. The main factors influencing the price negotiation between farmers and collectors are the world price and the volumes/quality produced by French producers. This world price is based on • the volumes produced (mainly the volume produced in the Northern hemisphere in July, and to a lesser extent the volume produced in the Southern hemisphere in December/January), •the state of the world wheat stocks, • the state of the global demand for wheat (both for milling wheat and for feed wheat), • the production costs of the most efficient competitors on the world market (currently Russia) and •the extant trade policies (tariff barriers, import quotas). The most influential publication on world prices is the “World Agricultural Supply and Demand Estimates” (WASDE) published every month [97]. In terms of policies, cereal production is dominated by tariff barriers. This barrier can be lower for quotas negotiated for some countries. Trade agreements on these quotas therefore have a strong influence (e.g., future negotiations with Ukraine) on both the evolution of prices of wheat and volumes of wheat produced in France and the EU. Agriculture 2022,12, 280 20 of 30 The policies of the importing countries, which may vary or even undergo significant changes (crisis, embargo, etc.), may also cause variations in the market. The price of oil, impacting both the price of farm inputs and transport, also has a strong impact on world prices. The euro/dollar exchange rate also influences the ability of EU countries to export. The ability to reach the quality required for milling wheat (as measured by humidity, Hagberg index, baking strength and, in particular, protein content rate) depends on: (i) the evolution of requirements for these indicators (especially protein rate); (ii) the climate (which influences most aspects of wheat quality); and (iii) fertilisation conditions (which influence the protein rate). The fertilisation conditions are framed by the nitrate directive, which regulates the level of nitrogen input allowed. This directive can therefore influence the priorities of selection of wheat varieties, by motivating the favouring of varieties which optimise the use of nitrogen. For some years, there has been an obligation for collectors to state the protein content in contracts, in order to apply differential tariffs according to this protein level. The effect of the protein rate on the market is as follows: • if the protein rate is low: the price on the domestic market decreases and possibilities for export decrease, increasing the stocks of unsold wheat; and • if the protein rate is high: prices rise on the domestic market and the possibilities for export increase. Pesticide residues, heavy metal, and mycotoxin levels are analysed for wholesale market allotments. When these sanitary controls are negative, the wheat is very rarely destroyed, but simply mixed with other healthier volumes. Climate also influences the annual volume and quality of wheat produced. For example, if spring is too rainy in France, the wheat will be potentially inappropriate for milling (and will therefore partly be assigned to feed), which may raise the issue of supply for millers and the issue of proper valuation for producers. The global supply of wheat and the quality of this wheat influence the way it is valorised. On the domestic market, wheat firstly is processed by millers (around 6MT/year), and then sold for feed, starch production or biofuels. On the international market, wheat is mainly used for bakery, if it reaches the protein rate requirements of buyers. The price differential between bread wheat and feed wheat depends on the world price of corn and feed barley. Two other important factors influencing the average price for wheat between farmers and collectors are the storage/sales policy and the use of hedging tools (futures markets). Finally, the price negotiation for wheat differs according to the production contracts negotiated before the campaign. The premium value added to the average price of wheat is less for red label than it is for no pesticide residues chains. For organic wheat, the evolution of the price follows different market dynamics: most of the time, organic wheat is twice the price of conventional wheat, but the ratio between the two productions can evolve differently from year to year. The volumes of organic wheat are low at present, but the growth rate is substantial. The question, as for any differentiation, is “will the premium be kept if the differentiated production becomes the norm”? And if not, at which level of differentiated production (compared to overall production) will the level of premium become negligible? 5.3.2. Factors Influencing the Price Negotiation for Wheat between Collectors and Millers The price negotiation for grain between collectors (cooperatives or wholesalers) and millers mainly depends on the wheat prices (and therefore on average campaign prices). The premium prices are valorised all along the chain, involving different prices for wheat and different prices for the flour. Big mills may get cheaper prices for grain than medium and small mills, but to a small extent, as collectors try to keep their margins positive. The milling industry in France is very concentrated and most wheat production for milling is collected by the biggest cooperatives, then processed through their own mills. More than 50% of flour production is ground by millers that belong to collectors. However, even in this integrated situation, millers negotiate prices for wheat allowing them to keep their margins at the mill level, so that they can generate profits to invest in modernisation Agriculture 2022,12, 280 21 of 30 of the mill. Mills are managed as if they were independent production units. In terms of strategies (of both sourcing and selling), one can distinguish small, medium, and large millers (4–5 millers grind around 50% of the production which is turned into flour) [8]. Two different markets must be distinguished: (i) the traditional bakery market, with prices including both flour and services (financial, advisory and logistic) offered to bakers by millers; (ii) the industrial bakery and the retailer market, where prices of flour are about half those for the traditional bakery market. The contracts are fully indexed to the price of wheat. However, the market of flour is an over-the-counter market, meaning that it is not transparent at all compared to the wheat market. Medium size mills have the best economic conditions: they mainly—but not only—work with traditional bakeries (allowing them to better valorise flour) and specialise in specific kind of flour. Large mills have a weaker economic position, especially those which invested the most in flour export in the 1980’s, as export is now marginal, meaning that their structural expenses are somewhat too high. Small mills only work with traditional bakeries but cannot supply the industrial market, and so are unable to increase their activity, with fierce competition among them. 5.3.3. Factors Influencing the Price Negotiation for Wheat between Millers and Bakers/Retailers Small and medium millers mainly sell their flour to traditional bakeries (medium bakers may also sell some volume to industry) while big millers mainly sell their flour to traditional bakeries within their franchise, industrial bakeries, and retailers. Traditional bakeries have low bargaining power and are considered as “price takers”. Industrial bakeries and retailers (which can purchase their flour from actors outside France, and in bigger quantities, follow the evolutions of the flour market) are usually able to purchase flour for lower prices (mainly influenced by EU competition on flour, as they less commonly purchase flour from outside EU, largely for quality reasons). The ability of millers to control a flour mix (the mix between flour and additives) that is adapted to a bakery puts them in a rather dominant position in the negotiation of flour prices. However, the larger the volumes purchased by the industrial bakers and retailers (compared to the volumes purchased by traditional bakeries), the less dominant is the millers’ position in this negotiation. 5.3.4. Factors Influencing the Price Negotiation for Bread The price of bread is influenced by several factors: (i) the price at which flour was purchased (which depends on the quality of flour purchased). Wheat represents around 5% to 7% of the price of bread. Flour (including wheat) represents about 15% of the price of bread (this proportion is about 30% for organic bread). This means that raw materials play a small role in the price of bread, relative to personnel costs and other factors; (ii) the quality of bread produced (which affects the level of labour force required); (iii) the local competition on bread/food items locally sold. This refers to several dimensions: the ability to park easily to buy bread (especially in suburban areas), the diversity of baked products proposed, etc.; (iv) the position/importance of bread in the overall market strategy of the actor selling bread. For example, in the case of retailers, data from the OFPM (the French Observatory of Price and Margins Formation for Food Products) shows that the sale of bread in supermarkets can be done without any margin at the scale of the bakery section, with the simple aim of being able to offer bread to customers within the entire shopping basket purchased. 5.4. Subsequent Steps in Developing the Hybrid SD and ABM Hybrid Model The structure of the generic FVC model is based on the problem at hand (the fairness issue) and the objective of the modelling (test fairness improvement options with different experimental factors based on scenarios). Therefore, the agents that are part of the model Agriculture 2022,12, 280 22 of 30 and the agent decisions that are relevant for the modelling are selected based on the problem statement and the modelling objectives. After developing the cognitive map, the agents are further defined in terms of the properties that define them (agent attributes), the behaviours that cause state changes (decisions) and the interactions resulting from these behaviours (activities). While this paper does not treat in detail the subsequent quantitative modelling steps in the modelling process, a brief overview is given here to provide a flavour of the work involved and help set an agenda for future research. The main financial and production resources and variables for a generic FVC actor are shown in Figure 7as two interlinked modules on the left side (the system dynamics part), while the relevant ABM components such as agents’ decisions and agents’ parameters are presented on the right side. Agriculture 2022, 12, x FOR PEER REVIEW 23 of 31 Figure 7. A hybrid of SD and ABM simulation modelling conceptualisation for a generic FVC (Authors’ own conceptualisation adapted from VALUMICS project’s deliverable [67]). System dynamics flows of products and finance are on the left, and agent attributes and decisions on the right. This figure, which applies to each agent category in each FVC, shows: production inventory (in fact, there is one such module for each of conventional production, e.g., industrial baking or farming using pesticides, and alternative production, e.g., traditional baking or organic/ecological farming), at bottom left; financial resources balance, top left; and agents (attributes and behaviour), right. The production inventory module is related to the quantities of products planned and supplied, and adjusted according to demand (orders), capacity to produce and average rates of utilisation, including time to produce and time to supply to buyer. The financial resources module is related to the financial balance for each agent and depends on revenues (a function of product price, quantity bought, time to obtain payment and public financing) and expenses for production (which depends on variable costs for production, and any additional expenses agreed or coming from unfair trading behaviour). The production variable costs are connected to monetary values of production input resources, energy needed for production, labour needed for produced quantities and other costs such as production license costs or ecological standardisation taxes or equipment costs. To differentiate between conventional production and alternative production, we need to have monetary values for conventional and alternative input resources, traditional and renewable energy and payment rate per worker including social security and health care taxes. To parameterise the production inventory and financial resources modules, the following information is needed: average or min to max values for product quantities produced every production period (once or more times per year), and euros received and spent per production volume per relevant time period (every month or three months or per year or other). For the agent module, it is necessary to clearly define the agent’s attributes (parameters such as name and number of agents in each category and values for the main variables in the production and financial resource modules), and the agent decision routines controlling or managing the variables from these modules. The decisions are fleshed out in agent decision tables. Table 2 illustrates the decisions of agents in a generic FVC. These tables are tools for collecting and organising qualitative Figure 7. A hybrid of SD and ABM simulation modelling conceptualisation for a generic FVC (Authors’ own conceptualisation adapted from VALUMICS project’s deliverable [ 67 ]). System dynamics flows of products and finance are on the left, and agent attributes and decisions on the right. This figure, which applies to each agent category in each FVC, shows: production inventory (in fact, there is one such module for each of conventional production, e.g., industrial baking or farming using pesticides, and alternative production, e.g., traditional baking or organic/ecological farming), at bottom left; financial resources balance, top left; and agents (attributes and behaviour), right. The production inventory module is related to the quantities of products planned and supplied, and adjusted according to demand (orders), capacity to produce and average rates of utilisation, including time to produce and time to supply to buyer. The financial resources module is related to the financial balance for each agent and depends on revenues (a function of product price, quantity bought, time to obtain payment and public financing) and expenses for production (which depends on variable costs for production, and any additional expenses agreed or coming from unfair trading behaviour). The production variable costs are connected to monetary values of production input resources, energy needed for production, labour needed for produced quantities and other costs such as production license costs or ecological standardisation taxes or equipment Agriculture 2022,12, 280 23 of 30 costs. To differentiate between conventional production and alternative production, we need to have monetary values for conventional and alternative input resources, traditional and renewable energy and payment rate per worker including social security and health care taxes. To parameterise the production inventory and financial resources modules, the following information is needed: average or min to max values for product quantities produced every production period (once or more times per year), and euros received and spent per production volume per relevant time period (every month or three months or per year or other). For the agent module, it is necessary to clearly define the agent’s attributes (parameters such as name and number of agents in each category and values for the main variables in the production and financial resource modules), and the agent decision routines controlling or managing the variables from these modules. The decisions are fleshed out in agent decision tables. Table 2illustrates the decisions of agents in a generic FVC. These tables are tools for collecting and organising qualitative and quantitative information of the main agent decision routines identified through the cognitive and agent mapping techniques. Table 2. Example of agent attributes and decisions for a generic FVC. Agent Group Attributes Decisions Producers Production capacity, production cost, risk aversion, min margin, number of customers, number of suppliers Initial investment, capacity planning, raw material sourcing, raw material price negotiation, production planning, store or sell (to whom?), internal (own product) price setting, price negotiation Collectors Handling capacity, production cost, min margin, number of customers, number of suppliers Decide on own product price, offer price to a producer, sell to whom? Primary processors Processing capacity, production cost, min margin, number of customers, number of suppliers Initial investment, source commodity, process, offer price to seller, process, increase/decrease capacity, supply to market, decide on own product price, accept/decline price offer from customer Secondary processors Number of suppliers, number of customers, production cost, min margin, capacity Offer price to seller, decide on own product price Retailers Retail capacity, min margin, liquidity, cost, number of customers, number of suppliers Source processed product, offer price to seller, decide on own product price, Consumers Socio-demographic, diet preferences, willingness to pay, demand Want to buy product, accept/decline price of productChoice between different product e.g., conventional or alternative For each agent decision routine, the following needs to be clearly explained: •Main goal (decision routine purpose), •Level and timing of the decision, Agriculture 2022,12, 280 24 of 30 •Detailed procedural description of each food value chain agent decision routine, •Any quantitative parameters associated with the decision routine, • Main related factors (endogenous and exogenous variables) conditioning the decision routine, • What if questions (hypothetical changes in the system) that can affect the agent decision routine. The decision table then gives detailed information about each decision type for each agent in the food value chain being considered. The particular agent behaviours and decisions chosen for quantitative formulation are those relevant to the what-if questions identified as important for the problem to be modelled. These questions will then drive out the agent decisions that need to be modelled to answer the questions. The relevant agent decisions will then be modelled quantitatively and used to derive fully specified algorithms, represented either as pseudocode or (as in Figure 8) as formal flowcharts. Agriculture 2022, 12, x FOR PEER REVIEW 25 of 31 Figure 8. Example of decision rules for processor selling to retailer. Agent-specific variables and decisions are taken as endogenous to the modelled system and will produce the emergent behaviour of the whole system. Other variables (factors) such as stock exchange prices, global supply and demand, relevant macroeconomic indicators, and market regulation are exogenous to the modelled system; a requirement for further work is to develop understanding of their influence on the system components (on agent decisions related to price setting and negotiation, UTP, production quantities etc.). Future papers will present details of this ongoing work. 6. Discussion Major food systems need to contribute to the UN’s Sustainable Development Goals (SDGs) by increasing sustainable production efficiencies (more food with less impact), reducing food waste and loss, and shifting diets towards more plant-based food [4,5]. The Green Deal and the Farm to Fork strategy of the European Commission were launched with ambitious aims to tackle the challenges of the European food system and motivate transition to sustainable food system [6,7]. The simulation model described here is designed to assess the impact of intervention strategies e.g., for food value chain actors in future scenarios and how this may influence the gross profit margin and level of employment. An example is the implementation of regulations or policies set to influence transitions of the food system such as the Farm to Fork strategy to enhance the sustainability of European food systems. As discussed earlier, food system transitions towards sustainability depend on fair procedures and outcomes. Exploration of the interorganisational fairness problem needs to account for interconnected effects between distributional fairness and procedural fairness, and effects of related and unrelated regulation: (a) how fair or unfair interrelations affect fair or unfair value distributions; (b) how bargaining power affects distributional and procedural fairness; and (c) how related and unrelated regulations affect the degree of fairness in value distribution and procedural interrelations. Gross margin can be used as a proxy indicator for fair value distribution, and for procedural fairness the Lerner index can give an estimate of the degree of market power, while the sizes and numbers of suppliers or buyers and the availability of alternatives can indicate bargaining power position [13,14,25,77]. The problem researched here (fair value distribution and fair procedural interrelations among food value chain agents) is not isolated but is connected to the overall global food system challenges of making food value chains economically, environmentally, and socially sustainable and resilient [1,3,98,99]. Simulation can help decision makers to assess the impact of interventions aimed at enhancing sustainability and resilience and how Figure 8. Example of decision rules for processor selling to retailer. Agent-specific variables and decisions are taken as endogenous to the modelled system and will produce the emergent behaviour of the whole system. Other variables (factors) such as stock exchange prices, global supply and demand, relevant macroeconomic indicators, and market regulation are exogenous to the modelled system; a requirement for further work is to develop understanding of their influence on the system components (on agent decisions related to price setting and negotiation, UTP, production quantities etc.). Future papers will present details of this ongoing work. 6. Discussion Major food systems need to contribute to the UN’s Sustainable Development Goals (SDGs) by increasing sustainable production efficiencies (more food with less impact), reducing food waste and loss, and shifting diets towards more plant-based food [ 4 , 5 ]. The Green Deal and the Farm to Fork strategy of the European Commission were launched with ambitious aims to tackle the challenges of the European food system and motivate transition to sustainable food system [ 6 , 7 ]. The simulation model described here is designed to assess the impact of intervention strategies e.g., for food value chain actors in future scenarios and how this may influence the gross profit margin and level of employment. An example is the implementation of regulations or policies set to influence transitions of the food system such as the Farm to Fork strategy to enhance the sustainability of European food systems. As discussed earlier, food system transitions towards sustainability depend on fair procedures and outcomes. Exploration of the interorganisational fairness problem needs to