Why are risk decisions sometimes rather irrational and biased than rational and effective? Can we educate and train vocational students and professionals in safety and security management to let them make smarter risk decisions? This paper starts with a theoretical and practical analysis. From research literature and theory we develop a two-phase process model of biased risk decision making, focussing on two critical professional competences: risk intelligence and risk skill. Risk intelligence applies to risk analysis on a mainly cognitive level, whereas risk skill covers the application of risk intelligence in the ultimate phase of risk decision making: whether or not a professional risk manager decides to intervene, how and how well. According to both phases of risk analysis and risk decision making the main problems are described and illustrated with examples from safety and security practice. It seems to be all about systematically biased reckoning and reasoning.
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In Eastern Africa, increasing climate variability and changing socioeconomic conditions are exacerbating the frequency and intensity of drought disasters. Droughts pose a severe threat to food security in this region, which is characterized by a large dependency on smallholder rain-fed agriculture and a low level of technological development in the food production systems. Future drought risk will be determined by the adaptation choices made by farmers, yet few drought risk models … incorporate adaptive behavior in the estimation of drought risk. Here, we present an innovative dynamic drought risk adaptation model, ADOPT, to evaluate the factors that influence adaptation decisions and the subsequent adoption of measures, and how this affects drought risk for agricultural production. ADOPT combines socio-hydrological and agent-based modeling approaches by coupling the FAO crop model AquacropOS with a behavioral model capable of simulating different adaptive behavioral theories. In this paper, we compare the protection motivation theory, which describes bounded rationality, with a business-as-usual and an economic rational adaptive behavior. The inclusion of these scenarios serves to evaluate and compare the effect of different assumptions about adaptive behavior on the evolution of drought risk over time. Applied to a semi-arid case in Kenya, ADOPT is parameterized using field data collected from 250 households in the Kitui region and discussions with local decision-makers. The results show that estimations of drought risk and the need for emergency food aid can be improved using an agent-based approach: we show that ignoring individual household characteristics leads to an underestimation of food-aid needs. Moreover, we show that the bounded rational scenario is better able to reflect historic food security, poverty levels, and crop yields. Thus, we demonstrate that the reality of complex human adaptation decisions can best be described assuming bounded rational adaptive behavior; furthermore, an agent-based approach and the choice of adaptation theory matter when quantifying risk and estimating emergency aid needs.
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The focus of this project is on improving the resilience of hospitality Small and Medium Enterprises (SMEs) by enabling them to take advantage of digitalization tools and data analytics in particular. Hospitality SMEs play an important role in their local community but are vulnerable to shifts in demand. Due to a lack of resources (time, finance, and sometimes knowledge), they do not have sufficient access to data analytics tools that are typically available to larger organizations. The purpose of this project is therefore to develop a prototype infrastructure or ecosystem showcasing how Dutch hospitality SMEs can develop their data analytic capability in such a way that they increase their resilience to shifts in demand. The one year exploration period will be used to assess the feasibility of such an infrastructure and will address technological aspects (e.g. kind of technological platform), process aspects (e.g. prerequisites for collaboration such as confidentiality and safety of data), knowledge aspects (e.g. what knowledge of data analytics do SMEs need and through what medium), and organizational aspects (what kind of cooperation form is necessary and how should it be financed).Societal issueIn the Netherlands, hospitality SMEs such as hotels play an important role in local communities, providing employment opportunities, supporting financially or otherwise local social activities and sports teams (Panteia, 2023). Nevertheless, due to their high fixed cost / low variable business model, hospitality SMEs are vulnerable to shifts in consumer demand (Kokkinou, Mitas, et al., 2023; Koninklijke Horeca Nederland, 2023). This risk could be partially mitigated by using data analytics, to gain visibility over demand, and make data-driven decisions regarding allocation of marketing resources, pricing, procurement, etc…. However, this requires investments in technology, processes, and training that are oftentimes (financially) inaccessible to these small SMEs.Benefit for societyThe proposed study touches upon several key enabling technologies First, key enabling technology participation and co-creation lies at the center of this proposal. The premise is that regional hospitality SMEs can achieve more by combining their knowledge and resources. The proposed project therefore aims to give diverse stakeholders the means and opportunity to collaborate, learn from each other, and work together on a prototype collaboration. The proposed study thereby also contributes to developing knowledge with and for entrepreneurs and to digitalization of the tourism and hospitality sector.Collaborative partnersHZ University of Applied Sciences, Hotel Hulst, Hotel/Restaurant de Belgische Loodsensociëteit, Hotel Zilt, DM Hotels, Hotel Charley's, Juyo Analytics, Impuls Zeeland.
The Dutch main water systems face pressing environmental, economic and societal challenges due to climatic changes and increased human pressure. There is a growing awareness that nature-based solutions (NBS) provide cost-effective solutions that simultaneously provide environmental, social and economic benefits and help building resilience. In spite of being carefully designed and tested, many projects tend to fail along the way or never get implemented in the first place, wasting resources and undermining trust and confidence of practitioners in NBS. Why do so many projects lose momentum even after a proof of concept is delivered? Usually, failure can be attributed to a combination of eroding political will, societal opposition and economic uncertainties. While ecological and geological processes are often well understood, there is almost no understanding around societal and economic processes related to NBS. Therefore, there is an urgent need to carefully evaluate the societal, economic, and ecological impacts and to identify design principles fostering societal support and economic viability of NBS. We address these critical knowledge gaps in this research proposal, using the largest river restoration project of the Netherlands, the Border Meuse (Grensmaas), as a Living Lab. With a transdisciplinary consortium, stakeholders have a key role a recipient and provider of information, where the broader public is involved through citizen science. Our research is scientifically innovative by using mixed methods, combining novel qualitative methods (e.g. continuous participatory narrative inquiry) and quantitative methods (e.g. economic choice experiments to elicit tradeoffs and risk preferences, agent-based modeling). The ultimate aim is to create an integral learning environment (workbench) as a decision support tool for NBS. The workbench gathers data, prepares and verifies data sets, to help stakeholders (companies, government agencies, NGOs) to quantify impacts and visualize tradeoffs of decisions regarding NBS.
Over the past decade, the trend in both the public sector and industry has been to outsource ICT to the cloud. While cost savings are often used as a rationale for outsourcing, another argument that is frequently used is that the cloud improves security. The reasoning behind this is twofold. First, cloud service providers are typically thought to have skilled staff trained in good security practices. Second, cloud providers often have a vastly distributed, highly connected network infrastructure, making them more resilient in the face of outages and denial-of-service attacks. Yet many examples of cloud outages, often due to attacks, call into question whether outsourcing to the cloud does improve security. In this project our goal therefore is to answer two questions: 1) did the cloud make use more secure?and 2) can we provide specific security guidance to support cloud outsourcing strategies? We will approach these questions in a multi-disciplinary fashion from a technical angle and from a business and management perspective. On the technical side, the project will focus on providing comprehensive insight into the attack surface at the network level of cloud providers and their users. We will use a measurement-based approach, leveraging large scale datasets about the Internet, both our own data (e.g. OpenINTEL, a large- scale dataset of active DNS measurements) and datasets from our long-term collaborators, such as CAIDA in the US (BGPStream, Network Telescope) and Saarland University in Germany (AmpPot). We will use this data to study the network infrastructure outside and within cloud environments to structurally map vulnerabilities to attacks as well as to identify security anti-patterns, where the way cloud services are managed or used introduce a weak point that attackers can target. From a business point of view, we will investigate outsourcing strategies for both the cloud providers and their customers. For guaranteeing 100% availability, cloud service providers have to maintain additional capacity at all times. They also need to forecast capacity requirements continuously for financially profitable decisions. If the forecast is lower than the capacity needed, then the cloud is not able to deliver 100% availability in case of an attack. Conversely, if the forecast is substantially higher, the cloud service provider might not be able to make desired profits. We therefore propose to assess the risk profiles of cloud providers (how likely it is a cloud provider is under attack at a given time given the nature of its customers) using available attack data to improve the provider resilience to future attacks. From the costumer perspective, we will investigate how we can support cloud outsourcing by taking into consideration business and technical constraints. Decision to choose a cloud service provider is typically based on multiple criteria depending upon the company’s needs (security and operational). We will develop decision support systems that will help in mapping companies’ needs to cloud service providers’ offers.
