Faced with the complexity and uncertainty of causal interactions between observable economic and managerial phenomena, modeling techniques continue to spread and diversify. Currently, these techniques are even the subject of a booming market (sales of modeling software; provision of modeling and simulation services; consulting services provided by specialized consultancies...).
In many cases, the study of systems (processes or real phenomena) is carried out through their modeling, i.e. their description through the creation of models allowing to explain them, to explore their behaviors, even to monitor or control their future evolution. In fact, modelling aims not only to promote a better understanding of economic, organizational and managerial behaviours, but also, through the best knowledge it brings, to act on them. In practice, it consists in designing and using qualitative or quantitative models for which there are means of evaluating performance according to the expected knowledge (theoretical or practical cognition).
The term model refers to any representation of the system/phenomenon studied. In economics, this representation remains structurally close to a reality apprehended both by theory and by quantified observation, which enables the model to play the role of an intermediary instance of empirical validation of theories in order to identify the laws governing the functioning of economies and the interdependencies underlying the various economic spheres, particularly macro-economic and financial.
Economic models thus make it possible to implement tools that help explain economic processes and their endogenous dynamics, or even simulate the effect of certain random shocks on economic systems (such as economic crises resulting from unknown factors or disconcerted political actions As for validation, this is carried out through the massive use of of formalisms and quantification techniques statistical and econometric (graphs, indices, correlations, regressions, classification, structural equations, etc.) in association with scientific hypothetico-deductive or inductive approaches.
However, in the management sciences, the general understanding of models takes on other visions and other directions. Models are presented as assistance tools whose explicit aim is to aid decision-making, the mathematization of social behavior and the rationalization of operating, coordination or organizational procedures (operational research, game theory, etc.). Their applications touch on a vast field of management disciplines, such as organizational planning, resource allocation, economic exchanges through input-output tables, project management, etc. Mobilizing mathematicians and managers, the impact of models finds its strength in a good articulation of individual supposed rational decisions and collective choices placed in general under a manifest rule of optimization, reflecting the requirements of effectiveness, efficiency and quality in any management system.
In addition, another type of modelling which has grown in importance with advances in computer science and related technologies is based on algorithms1. In fact, the computational turn and the advantages of algorithmics may have favoured the role of models in the treatment of random processes (complex statistical estimates, simulation), allowing decisions in situations of uncertainty and the dynamics of individual interactions, or even real-time decision-making promoted by massive flows of data processed with high reliability and velocity. The final output, in this sense, are in the form of counting and analysis of statistical data, graphical (2D/3D) or schematic visualizations more or less simple. This IT transformation has therefore profoundly modified both model design and modeling and simulation practices, and has accentuated the fact of considering models no longer as intermediaries towards a theory, but as autonomous agents and focus on the dynamics of model construction and manipulation (Morgan and Morrisson, 19992).
George Box's quote (1976) that "all models are wrong, but some are useful!" underlies the presence of inherent limitations in the modeling activity. Somewhere in the literature, there is talk of conceptual or application failures within modeling in economics and management. It is first of all the reductionist character of the models where only some aspects of the system or the real phenomenon are taken into account in order to have rather simplified formats. Then, the unstable nature of the theoretical parameters, which could affect the effective representativeness of the models following the failure to take into account the changes incurred. Or, it can be limits related to the statistical methods used in the modeling process, whether at the level of the specification of the variables (risk of omission of primordial variables or founding assumptions of the theory), or at the level of empirical validation (lack of rigour in the implementation of statistical methods), or also at the level of the use of certain methods rather than others.
In addition, one of the limitations that could constitute a major challenge to the extension and successful deployment of modeling activity in the economic sphere of developing countries, like Morocco, concerns the failure of the transposition of economic models and management from North to South. Certainly, the models implemented for the countries of the South and in which it is not taken into account the diversity of territories and cultures, the specificity of environments and especially the consequent vitality of informal economies so widespread, exclude, or at the rigor, reduce the participation of modelling actors and hinder experimentation with their own creative and learning capacities, which leads to shortcomings that can taint decisions made at the level of national economic policies.
Moreover, modeling in economics and management is at the heart of the work of Moroccan researchers, actors and decision makers who apprehend it with their own visions and objectives. Being locally and temporally located, modeling activity continues to evolve in interaction with the scientific, cultural and territorial context. Political, economic and social factors, as differentiated as they are, are considered to be important sources of influence on the orientations of the models put in place and on the degree of economic or managerial benefits resulting from them.
This international event is a real crossroads where ideas, experiences, theories and practices can be exchanged, enriching the fields linked to modeling when placed in an economic or managerial context.
Thus, the conference aims for a first purpose, To highlight research axes that favor the analysis and criticism of modeling practices and models which are increasingly used in economic analysis and evaluation and in the decision-making of organizations.
As such, an interrogation deserves to be raised on the cognitive approaches of these practices and on the epistemological and social foundations that guarantee the reliability of these models when applied to domains, such as economics, where plural theories converge and interact. At a time when economic and management models are increasingly accessible, particular attention is being paid to work highlighting the benefits that modelling can offer to the development of scientific research in general, in particular, and on the other hand, to the challenges and concerns of the adequacy of the use of models in economic and managerial circles. In this respect, questions need to be asked about the cognitive approaches to these practices, and about the epistemological and social foundations that guarantee the reliability of these models when applied to fields such as economics, where plural theories converge and interact.
At a time when economic and management models are becoming increasingly accessible, particular attention is paid, on the one hand, to work highlighting the advantages that modeling can offer to the development of scientific research in general, and national research in particular, and on the other hand, to the challenges and concerns of adequacy relative to the use of models in economic and managerial circles. Then, for a second purpose, it is a question of emphasizing the importance of modern modeling methods, of simulation, algorithmics and artificial intelligence in conjunction with the global digital transition strategy of global economies while taking stock of the main aspects of their practicality and limitations as well as their benefits in terms of economic development and performance of organizations, particularly Moroccan.
And for a last purpose (which would not necessarily be the ultimate one), it is a question of provoking debate on the importance of collective construction of modeling tools (or adaptation of existing models) at the territorial level taking into account the many issues and specificities of socio-economic, cultural or environmental types, in order to assist regional actors and decision-makers in providing answers to the economic and territorial management needs of great scope.
- With three major aspects: the pluralization and massification of data flows (Big Data), new computing architectures, the rise of digital technologies in artificial intelligence and integrative simulation techniques.back
- MORGAN M., MORRISSON M., 1999 "Models as mediators : Perspectives on natural and social science", Cambridge, Cambridge University Press.back