In Tribute to Peter Naur: An Essay on Human-Centric Decision Making

1 In Tribute to Peter Naur: An Essay on Human-Centric Decision Making Fernando Guilherme Silvano Lobo Pimentel Bank of Portugal, Lisbon, Portugal fpiment[email protected]t ORCID: 0000-0001-5891-1008 Version: v1.0 (Conceptual essay) Date: 22 October 2025 License: CC BY 4.0 Keywords: Computing, Human thinking, Synapse State Theory, Decision making, Personal reflection Abstract This text examines the human versus automatic nature of decision-making. It begins by outlining what constitutes a decision problem and offers a brief, introductory attempt to connect two views of how we form habits and make decisions: Peter Naur’s Synapse-State Theory and António Damasio’s Somatic Marker Hypothesis. These approaches to structuring human thought and decision-making are presented as complementary. Taken together, they highlight the qualitative, adaptive nature of human decision-making and habit formation, in contrast to the automatic systems of computers and artificial intelligence, thereby contributing to a human-centric view of decision and behavior formation. We include a brief exercise and elements of personal reflection to complement these views. Suggested citation Pimentel, F. G. S. L. (2025). In Tribute to Peter Naur: An Essay on Human-Centric Decision Making. Zenodo. Author note A conversation held with Peter Naur in a scientific encounter in Heidelberg (Naur, 2014) were determinant to the writing of this independent (not endorsed) homage to him, for his kindness and inspirational thoughts. Provenance & Novelty Note This text is a conceptual synthesis and reflection. It restates and aligns ideas that may be established in prior literature (e.g., links between affect, plasticity, habit, and choice). Any elements presented without explicit citation should be read as part of the enquiry narrative and not as assertions of novelty or priority. Illustrative items like the poll and the LLM response are didactic, not empirical evidence. 2 Introduction No matter what their scope, importance and difficulty, decisions are typically informed by available information. Computers, dealing with data with their impressive resources, are sometimes valuable here. Decision problems, however, could be more than only common decisions, requiring something typically different in nature. Considering the available data or computations a decision problem is one where the alternative options still hold a similar value between them (Pimentel, 2011). Otherwise, a decision would be easy. Since it is not, we say we have a decision problem. One whose solution lies in information internal and not external to the human mind. Following this idea, in this work we will weight arguments suggesting whether decision making is at its core a human process, rather than a computing one. This is particularly the case when there is no data available, or when there is not enough data, or when the data does not permit the extraction of optimal solutions from trade-offs between decision criteria (Phillips, 2007). Given a decision problem, when it becomes necessary to weight the pros and cons of each course of action, there is one sign that the human mind is hard to replace. It is the fact that there is a whole “Decision making” discipline within the Operations Research academic field, where “Decision makers” not the data, are the ones from which to extract the objectives and criteria, sometimes with the help of other humans - the facilitators or consultants. Computers can complement that role for example by validating the mathematical consistency of human judgements through a software like MacBeth (Bana e Costa, 1994). The trade-offs between decision criteria are where part of the decision subjectivity lies, at the core of the decision-making process. The nowadays so popular neural networks and artificial intelligence (AI) rely on weights (features weights) with which they parametrize their reasoning like in the backpropagation algorithm (Rumelhart, 1986). Massive amounts of data produce these weights. Curiously, in large language models based in AI, like the famous ChatGPT, this process also depends on a validation moment, when a person gives feedback to the system, to let the machine know how to adjust such parameters. Therefore, even that machine decision making process is dependent on human intervention. One problem with quantitative or data-centric methods is the difficulty of learning (in a model-like way) from trade-offs because they are not causal in nature, and because there are different alternatives to each solution within a small reach (Pimentel, 2011). The purpose of this essay is to question whether, through a simplified and biologically motivated scheme, an exercise, and a Personal reflection, the human machinery is naturally equipped to deal with decision problems in a better way than decision algorithms do. The modern world is one where digitalization contributes to a growing information overload (Arnold, 2023). Media, social networks, artificial intelligence, and computers replaced decision making processes previously made on another basis. While information is helpful, it often does not substitute the role of emotions. There is a common perception that we should reduce screen time. Too much is harmful (Stiglic, 2019), and it does not necessarily help to decide. Evidence already points the other way around - studies suggested that excessive screen time can diminish orbitofrontal cortex thickness (Lee, 2018), affecting memory and cognitive functions, such as decision-making. As an alternative, the psychological point of view suggested that exposure to nature promotes healthy decision making (Berry, 2020). 3 In his paper “Computing versus human thinking”, Peter Naur pinpointed the plasticity of the elements of the nervous system as the main factor distinguishing the machine processing a text like this one from the flow of human thought behind it. His “Synapse State Theory” (Naur, 2007) describes the brain as made of neurons, synapses, and nodes (neurons terminals). Neurons transmit brain activity through synapses towards the summation elements – the nodes. The plastic element corresponds to the conductivity of the synapses. The more conductive they are between two elements, the stronger the links associating them. According to Peter Naur, in his own words “if you want to form a habit, you must insist” (Naur, 2014). So, we know this is true from experience. The links become stronger and do not back off, like when we model plasticine. Unlike the digital nature of computers, with a discrete nature, in Naur’s description, here very much synthetized – the human brain works in a plastic way. If we record its “deformation”, it is fundamentally continuous in nature. The Somatic Marker Hypothesis introduced by António Damasio (Damasio, 1996) underlined the role of emotions in decision making by the brain. This process starts with emotions in the body that correlate to feelings (somatic markers). For example, a shiver associated with fear, which activates certain connections in the brain – resembling synapses in Peter Naur’s model – thereby motivating a certain course of action – the one perceived as most rewarding. 2. Background We have designed our approach around the following elements: • A synthesis of pre-existent and stablished ideas: o We gathered two scientific hypotheses on the brain functioning and included them in one single viewpoint. • An exercise to assess the argument of the eminently human nature of decision making. • A Personal reflection on our own experience 3. Conceptual integration 3.1 A synthesis We propose there is a certain complementarity between Damasio’s work on the role emotions play in decision making (Damasio, 1994) and Peter Naur’s synapse state theory (Naur, 2007). The link might be emotions (Damasio, 1994), elements that through flagging certain events as important can strengthen the conductivity of the synapses relevant to them. After an initial association, an explanation to the plasticity we referred is the following: Next time an analogous situation occurs, the same feeling takes place in the body, and the emotions attached to it (Damasio, 1996) can flag events that reinforce previous brain connections (synapses). However, these are now stronger, the conductivity of the synapses involved increases. It does not start from zero. The brain “knows” this is not something new. 4 And that can explain the plasticity of the synapses behind the formation of a habit (Naur, 2007), the same plasticity that models future decisions. As an illustration of what we have just said, in the following scheme (figure 1) we propose a unification of the somatic marker hypothesis (Damasio, 1996) and the Synapse state theory (Naur, 2007). The steps in green concern Damasio’s view and the steps in blue concern Naur’s one. The third color (gray) represents feedback loops typically associated with memory. From the following diagram (figure 1) we exclude the almost instantaneous decision processes involved in the “fight or flight” decisions which activate the amygdala and hijack usual body functions involved in decisions (McCarty, 2016). Figure 1 Formation of habits and decisions – synthesis schema articulating Damasio (in Green) and Naur’s (in Blue) hypothesis (excluding “fight of flight” decisions) 3.2 Illustrative vignette The synthesis we proposed places the human mind at the center of the decision making process, with the interference of such subjective things as emotions, events, and their memorization, which can be loaded with meaning for some but not for others. Abstracting from the model, and to shed light on whether such biologically and subjective machinery can get different from computers, we have carried an exercise. We selected a Feeling End Emotion flags certain events? No yes Synapses related to those events become stronger. First time? Start Set synapses conductivity yes Increase Synapses conductivity No Decision Another step in Habit formation Plastic element End Feedback Loop 5 question, considering that figuring out the answer would be a decision problem for the respondents. We compared the answer of a computer, given through the ChatGPT language model, through its version freely made available on September the 5th 2024 on the Internet by OpenAI company, with fifty human answers to the same question collected through Mentimeter (Mentimeter, 2023). The question posed to both types of respondents was: “If you had to say in one word what marriage is all about, what would that word be?” ChatGPT gave an unexpected answer: “Partnership” According to the Oxford English dictionary, known as the most reputable English dictionary in the world, partnership is “the state of being a partner in business.” So, the automatic answer of an algorithms system, known for its abilities to interpret language, reduced marriage to a business. Through Mentimeter.com (Mentimeter, 2023) we collected the 50 responses given by humans in February 2023, during a wedding preparation course, made possible by Oeiras Verbum Dei Community and Paço de Arcos Parish in Lisbon. The answers were anonymous. We collected them in the beginning of the course, before delivering any material. The following graphic represents (in Portuguese) the answers (and their frequency): Figure 2 Synthesis of 50 human responses to the question “Define marriage in one word”. Letter size indicates how many times each word was chosen. Thicker letters indicate more frequent responses. The top five words chosen were: • Unity • Love • Sharing • Trust • Family 6 Having to choose a word that defines something resembles having to decide, and despite the massive amount of data at the disposal of ChatGPT, the human responses appeared more precise in this instance. There were no business-like words in the definitions of marriage given by humans. This exercise suggests not to the fact that ChatGPT is imprecise, because it is still a very powerful and useful tool, but to the fact that the human and natural treats of decision-making that figure 1 illustrates seem valuable enough to deserve consideration as an alternative to processes based on data. 3.3 Personal reflection (meaning in decision) Beyond optimizing explicit objectives, human decision making involves moments when attention, prior experience, and personal meaning cohere into a directional “feel.” Current decision systems capture utilities and constraints but not this layer (rationales, reflective pauses, and annotations of “what mattered” at decision time). The brief examples below illustrate how meaning can shift attention and perceived options; they do not constitute empirical evidence. In a period of uncertainty, we noticed that small cues—an unexpected association, a remembered sentence, a visual detail—would sometimes re-weight the field of options. What looked like equal alternatives a moment before became asymmetrical, not because new data appeared, but because a pattern of meaning clicked into place: a connection between prior experience, current aims, and what we cared about. The shift could come from: (i) attention tightening around a cue such as a very unlikely coincidence (ii) feelings of visceral comfort/discomfort (iii) memory offering adjacent examples; (iv) the option set re-composing— in the light of new information. These events did not “prove” anything; they simply changed what could be seen as reasonable to do next. This first-person description of experience is used here to illustrate a decision layer that is not well represented in formal pipelines. It is not offered as evidence and does not ground any claims of effectiveness. 4. Implications for practice Variables that assume exact amounts are the basis of discrete systems like computers. The idea of a flexible, “plastic” and “emotional” brain seems to better capture the very essence of human decision making. A soft methodology that finds ultimate criteria based on values (Pimentel, 2011), just like the feedback loop in figure 1 illustrating synapses plasticity or learning from decisions, assumes that the brain, not the numbers make the decisions. The memory lessons that human decision making provides typically overcome those from data centric processes. Although these can also be based on lessons (from historical data), they are collected on the basis of correlations, which do not imply causation, so we do not really get to know what was on the basis of a decision’s outcome, not as naturally as we can learn from our own body. The previous arguments point to a different nature between input data for an AI training model and human (brain) like decisions. And our proposal of a complementary view of the 7 two scientific hypotheses of Damasio and Peter Naur underlines the distance between human and computer thinking. We propose that the higher flexibility of the human mind makes it better equipped to manage decision problems. That flexibility allows ‘internal states’ of insufficiency in different degrees, while machines (in strict Boolean logic) can only be in correct or incorrect sates (Hill, 2014). “Insufficiency” states in human decision making occur when prioritizing points of view which can be partially unfulfilled. Therefore, the acceptance of insufficiency, something typically human and linked to vulnerability, can inform decision-making in a way that a machine cannot. Relying on data to question if decision making is more human-centric than data-centric could be a paradox. Therefore, the human experience elements presented last as a complement to the synthesis in the model sound like be a better ground. This reflection shows empirical directions to study how attention, feeling, and memory act together to restructure the option set. 5. Conclusion The purpose of this essay was to consider the human versus artificial nature of decisions. We started by arguing the natural use of the human element in real decision problems (those with alternatives similar in value) given the biological machinery of our brain functions, adapted to decision and habit forming. Both the theoretical viewpoint, from the hypothesis we aligned, and the exercise element, pointed towards the idea of decisions being human centric rather than data centric processes. A diagram integrating two scientific hypotheses: the Synapse state theory and the Somatic marker hypothesis illustrated this from the perspective of a model which biologically qualifies past decisions to inform future ones. The feedback loops the body learns from - whether through an increase in synapses conductivity, or the perception of feelings detected in previous similar decisions resemble cause-effect relationships more than what data correlations do. In addition to that, an exercise deployed a situation where human responses were judged more aligned with the right concept priorities when compared to an advanced computer model (ChatGPT), supporting the idea of a natural predisposition of our minds to engage in decision making. This is a point in favor of less screen time and more attention to our own feelings, emotions, and even spiritual practice, as a feasible way to achieve better outcomes with our decisions, as the Personal reflection presented illustrated. Data Access Statement No datasets were generated or analyzed in this study. 8 References Arnold, M. & Goldschmidt, M. & Rigotti, T. (2023). Dealing with information overload: a comprehensive review. 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