Many decisions in modern societies have a very complex scientific basis. Clinicians have to choose between different drugs for treating a patient. Central bankers have to forecast the evolution of financial
markets, and to control the amount of money that circulates in a society. Physicists have to evaluate the impact of continued CO2 emissions for life on the planet. All these decisions are based on the
forecasts of scientific models, and sometimes, their predictions reach a great degree of exactness (e.g., in identifying high-risk hospital patients and allocating resources efficiently).
However, why should we rely on such models when they are highly idealized and contain assumptions that are far from the truth? What is it that makes decisions based on them reliable and trustworthy? How
do we factor in their intrinsic uncertainty? In short, how does science based on uncertain models contribute to good decisions?
Our project investigates the interface between modeling and decision-making. We develop an understanding of how scientific models function, how they advance our knowledge despite their intrinsic
uncertainty, and how they are interpreted in a decision context. More specifically, we focus on the following three questions, which correspond to our main targets:
1. How can highly idealized and intrinsically uncertain scientific models be successful in prediction?
2. Why can we trust and accept scientific models in spite of their intrinsic uncertainty and how should we factor in this uncertainty?
3. How should we synthesize actuarial, model-based judgment with human expertise in making practical decisions?
In answering these three questions, our projects integrates foundational philosophical analysis (e.g., rational criteria for theory acceptance), formal and conceptual analysis, and case studies about
construction and use of models in a number of relevant scientific disciplines like financial economics and evidence-based medicine.
The outcomes of our project explain the epistemic value of uncertain scientific models, and how they guide rational decisions. This is of utmost relevance in an age of science skepticism, where the authority
of scientists (and the model-based predictions they make) is often challenged by claims that models are intrinsically uncertain and hence the policies adopted on their basis are not trustworthy (e.g., global
warming, quantitative easing, and vaccination policies). The international reputation of our research team, its experience in interdisciplinary projects, and the collaborations with mathematicians, economists,
and medical scientists within the affiliated institutions, guarantee that our research objectives can be met and will substantially advance the state of the art.
The overall project is divided into three subprojects, each of which employs two postdoctoral researchers ("assegnisti di ricerca") and is coordinated by the leader of a local research unit. The subprojects
correspond to three different stages on the path from models to decisions: (1) the construction of idealized models and the evaluation of their intrinsic uncertainty and (mis)match with reality; (2) the
acceptance of a particular scientific model on a certain evidential basis; (3) the use of models in decision-making, including the cognitive pitfalls that arise when models represent uncertainty in probabilistic
terms, and the problem of synthesizing model-based judgment and human expertise. The project PI (J. Sprenger/UniTO) is in charge of coordinating the project, keeping the parts coherent and synthesizing
the results in a final book project.
In Subproject 1 (leader: G. Valente/PoliMi), we investigate how models are constructed in statistical physics and related disciplines with a strong decision component, such as climate science and
econophysics. These models typically exhibit a high degree of idealization. We identify where exactly they mismatch reality, and what kind of predictive ambitions they can have. In Subproject 2 (leader: G.
Cevolani/Lucca), we use the truthlikeness criterion for model assessment for explaining the epistemic and predictive value of highly idealized models (i.e., models that are unable to fully describe the intended
target system). We also apply our results to the case studies from climate science and financial economics addressed in Subproject 1: we show when one can rationally trust model predictions in those fields,
and we explain how public policy decisions in those fields should factor in the relevant uncertainty. Finally, in Subproject 3 (leader: C. Martini/UniSR) we investigate in a medical case study how model-based
information interacts with clinical (expert) judgment to aid decision making, including the formulation and use of clinical guidelines. The case study will focus on off-label prescriptions and will be supported by
the San Raffaele Hospital Research Centre.
2. Detailed description of the project: targets that the project aims to achieve and their significance in terms of advancement of knowledge,
state of the art and proposed methodology
RESEARCH TARGETS AND ADVANCEMENT OF KNOWLEDGE
Many decisions in modern societies have a very complex scientific basis. Clinicians have to choose between different drugs for treating a patient. Central bankers have to forecast the evolution of financial
markets, and to control the amount of money that circulates in a society. Physicists have to evaluate the impact of continued CO2 emissions for life on the planet. All these decisions are based on the
forecasts of scientific models, and sometimes, their predictions reach a great degree of exactness (e.g., in identifying high-risk hospital patients and allocating resources efficiently).
However, why should we rely on such models when they are highly idealized and contain assumprions that are far from "the truth"? What is that makes decisions based on them reliable and trustworthy? How
Ministero dell'Istruzione dell'Università e della Ricerca
MIUR - BANDO 2017 - 5 -
do we factor in their intrinsic uncertainty? In short, how does science based on uncertain models contribute to good decisions?
Our project investigates the interface between modeling and decision-making. We develop an understanding of how scientific models function, how they advance our knowledge despite their intrinsic
uncertainty, and how they are interpreted in a decision context. More specifically, we focus on the following three questions, which correspond to our main research targets:
1. How can highly idealized and intrinsically uncertain scientific models be so successful in prediction?
2. Why can we trust and accept scientific models in spite of their intrinsic uncertainty and how should we factor in this uncertainty?
3. How should we synthesize actuarial, model-based judgment with human expertise in making practical decisions?
In answering these three questions, our projects integrates foundational philosophical analysis (e.g., rational criteria for theory acceptance) with case studies about construction and use of models in relevant
scientific disciplines (e.g., mathematical idealization in physical models, decisions in evidence-based medicine). The outcomes of our project explain the epistemic value of uncertain scientific models, and
how they guide rational decisions. This is of utmost relevance in an age of "science skepticism" where the authority of scientists (and the model-based predictions they make) is often challenged by the claim
that these models are intrinsically uncertain and that policies adopted on their basis are therefore not trustworthy (e.g., global warming, quantitative easing, and vaccination efficacy). The international
reputation of our research team, its experience in interdisciplinary projects, and the collaborations with mathematicians and medical scientists within the affiliated institutions, guarantee that our research
objectives can be met and will substantially advance the state of the art.
SCHOLARLY BACKGROUND AND DESCRIPTION OF SUBPROJECTS
The three central elements of our project ---model construction, model acceptance and practical use of models in decisions--- have been studied in philosophy of science and epistemology for quite some time
(e.g., Frigg and Hartmann 2012; Magnani and Bertolotti 2017). Yet, there still remain outstanding issues concerning their status, which can be settled only by means of a systematic investigation of the
conceptual foundations and the actual use of scientific models. The project is structured in such a way to provide the sought-after overarching analysis. In fact, it is divided into three subprojects that aim to
answer the above questions. Each subproject comprises two parts: in general, Study A examines the relevant aspects of scientific modeling at a theoretical level, whereas Study B deals with the application of
decision-making procedures to concrete case studies. That assures that the interaction between the different components of the overall project will develop within each subprojects as well as across the three
subprojects. The picture here below illustrates the articulation of the proposed research and indicates how the various phases are mutually related.
markets, and to control the amount of money that circulates in a society. Physicists have to evaluate the impact of continued CO2 emissions for life on the planet. All these decisions are based on the
forecasts of scientific models, and sometimes, their predictions reach a great degree of exactness (e.g., in identifying high-risk hospital patients and allocating resources efficiently).
However, why should we rely on such models when they are highly idealized and contain assumptions that are far from the truth? What is it that makes decisions based on them reliable and trustworthy? How
do we factor in their intrinsic uncertainty? In short, how does science based on uncertain models contribute to good decisions?
Our project investigates the interface between modeling and decision-making. We develop an understanding of how scientific models function, how they advance our knowledge despite their intrinsic
uncertainty, and how they are interpreted in a decision context. More specifically, we focus on the following three questions, which correspond to our main targets:
1. How can highly idealized and intrinsically uncertain scientific models be successful in prediction?
2. Why can we trust and accept scientific models in spite of their intrinsic uncertainty and how should we factor in this uncertainty?
3. How should we synthesize actuarial, model-based judgment with human expertise in making practical decisions?
In answering these three questions, our projects integrates foundational philosophical analysis (e.g., rational criteria for theory acceptance), formal and conceptual analysis, and case studies about
construction and use of models in a number of relevant scientific disciplines like financial economics and evidence-based medicine.
The outcomes of our project explain the epistemic value of uncertain scientific models, and how they guide rational decisions. This is of utmost relevance in an age of science skepticism, where the authority
of scientists (and the model-based predictions they make) is often challenged by claims that models are intrinsically uncertain and hence the policies adopted on their basis are not trustworthy (e.g., global
warming, quantitative easing, and vaccination policies). The international reputation of our research team, its experience in interdisciplinary projects, and the collaborations with mathematicians, economists,
and medical scientists within the affiliated institutions, guarantee that our research objectives can be met and will substantially advance the state of the art.
The overall project is divided into three subprojects, each of which employs two postdoctoral researchers ("assegnisti di ricerca") and is coordinated by the leader of a local research unit. The subprojects
correspond to three different stages on the path from models to decisions: (1) the construction of idealized models and the evaluation of their intrinsic uncertainty and (mis)match with reality; (2) the
acceptance of a particular scientific model on a certain evidential basis; (3) the use of models in decision-making, including the cognitive pitfalls that arise when models represent uncertainty in probabilistic
terms, and the problem of synthesizing model-based judgment and human expertise. The project PI (J. Sprenger/UniTO) is in charge of coordinating the project, keeping the parts coherent and synthesizing
the results in a final book project.
In Subproject 1 (leader: G. Valente/PoliMi), we investigate how models are constructed in statistical physics and related disciplines with a strong decision component, such as climate science and
econophysics. These models typically exhibit a high degree of idealization. We identify where exactly they mismatch reality, and what kind of predictive ambitions they can have. In Subproject 2 (leader: G.
Cevolani/Lucca), we use the truthlikeness criterion for model assessment for explaining the epistemic and predictive value of highly idealized models (i.e., models that are unable to fully describe the intended
target system). We also apply our results to the case studies from climate science and financial economics addressed in Subproject 1: we show when one can rationally trust model predictions in those fields,
and we explain how public policy decisions in those fields should factor in the relevant uncertainty. Finally, in Subproject 3 (leader: C. Martini/UniSR) we investigate in a medical case study how model-based
information interacts with clinical (expert) judgment to aid decision making, including the formulation and use of clinical guidelines. The case study will focus on off-label prescriptions and will be supported by
the San Raffaele Hospital Research Centre.
2. Detailed description of the project: targets that the project aims to achieve and their significance in terms of advancement of knowledge,
state of the art and proposed methodology
RESEARCH TARGETS AND ADVANCEMENT OF KNOWLEDGE
Many decisions in modern societies have a very complex scientific basis. Clinicians have to choose between different drugs for treating a patient. Central bankers have to forecast the evolution of financial
markets, and to control the amount of money that circulates in a society. Physicists have to evaluate the impact of continued CO2 emissions for life on the planet. All these decisions are based on the
forecasts of scientific models, and sometimes, their predictions reach a great degree of exactness (e.g., in identifying high-risk hospital patients and allocating resources efficiently).
However, why should we rely on such models when they are highly idealized and contain assumprions that are far from "the truth"? What is that makes decisions based on them reliable and trustworthy? How
Ministero dell'Istruzione dell'Università e della Ricerca
MIUR - BANDO 2017 - 5 -
do we factor in their intrinsic uncertainty? In short, how does science based on uncertain models contribute to good decisions?
Our project investigates the interface between modeling and decision-making. We develop an understanding of how scientific models function, how they advance our knowledge despite their intrinsic
uncertainty, and how they are interpreted in a decision context. More specifically, we focus on the following three questions, which correspond to our main research targets:
1. How can highly idealized and intrinsically uncertain scientific models be so successful in prediction?
2. Why can we trust and accept scientific models in spite of their intrinsic uncertainty and how should we factor in this uncertainty?
3. How should we synthesize actuarial, model-based judgment with human expertise in making practical decisions?
In answering these three questions, our projects integrates foundational philosophical analysis (e.g., rational criteria for theory acceptance) with case studies about construction and use of models in relevant
scientific disciplines (e.g., mathematical idealization in physical models, decisions in evidence-based medicine). The outcomes of our project explain the epistemic value of uncertain scientific models, and
how they guide rational decisions. This is of utmost relevance in an age of "science skepticism" where the authority of scientists (and the model-based predictions they make) is often challenged by the claim
that these models are intrinsically uncertain and that policies adopted on their basis are therefore not trustworthy (e.g., global warming, quantitative easing, and vaccination efficacy). The international
reputation of our research team, its experience in interdisciplinary projects, and the collaborations with mathematicians and medical scientists within the affiliated institutions, guarantee that our research
objectives can be met and will substantially advance the state of the art.
SCHOLARLY BACKGROUND AND DESCRIPTION OF SUBPROJECTS
The three central elements of our project ---model construction, model acceptance and practical use of models in decisions--- have been studied in philosophy of science and epistemology for quite some time
(e.g., Frigg and Hartmann 2012; Magnani and Bertolotti 2017). Yet, there still remain outstanding issues concerning their status, which can be settled only by means of a systematic investigation of the
conceptual foundations and the actual use of scientific models. The project is structured in such a way to provide the sought-after overarching analysis. In fact, it is divided into three subprojects that aim to
answer the above questions. Each subproject comprises two parts: in general, Study A examines the relevant aspects of scientific modeling at a theoretical level, whereas Study B deals with the application of
decision-making procedures to concrete case studies. That assures that the interaction between the different components of the overall project will develop within each subprojects as well as across the three
subprojects. The picture here below illustrates the articulation of the proposed research and indicates how the various phases are mutually related.