Anticipatory Activity Research Articles

An Impact-Based Flood Forecasting System for Citizen Empowerment

This work addresses the critical issue of flooding, a significant natural hazard, consistently ranked highest in the 2023 World Risk Index. The annual onslaught of tropical cyclones and the associated abnormal rainfall threaten lives, and destroy crops and property, thereby causing great economic damage, demanding urgent and science-based decision-making. We introduce an impact-based flood forecasting system as a proactive anticipatory action (i.e., AA) measure, linking climate services and disaster risk management to mitigate extreme weather impacts. Developed by the University of the Philippines Resilience Institute, the National Operational Assessment of Hazards (NOAH) Center, and Gerry Bagtasa of the Institute of Environmental Science and Meteorology, this system advances disaster resilience efforts, leveraging science-based forecasts to prioritize vulnerable barangays (villages). Unlike traditional early warning systems, the proposed automated system predicts flooding one day in advance and assesses exposure levels based on population distribution. By utilizing global weather forecast models locally calibrated for Philippine conditions and 100-year rain return flood hazard maps for the Philippines, the system forecasts river inundated areas, enabling local government units (LGUs) and humanitarian organizations to prioritize preparations for communities and allocate resources during flooding events effectively. This system enhances the efficiency of disaster response planning, fostering a proactive and impactful strategy to address the recurring threat of flooding in the country. The system also underscores the role of open data in disaster resilience, exemplified by NOAH’s system to disseminating big data, which aligns with open governance principles. By encouraging transparency and stakeholder collaboration, data exchange is promoted, providing actionable information that fosters collaboration between the LGUs, humanitarian organizations, and other stakeholders. Cognizant of the importance of community engagement in disaster resilience, the newly developed impact-based flood forecasting system encourages community involvement by providing easily accessible information, which can be used to validate the forecasts based on local knowledge and experience. This research contributes to the urgent call for anticipatory action in the face of escalating extreme weather events globally.

Geospatial tools and data for health service delivery: opportunities and challenges across the disaster management cycle.

As extreme weather events increase in frequency and intensity, the health system faces significant challenges, not only from shifting patterns of climate-sensitive diseases but also from disruptions to healthcare infrastructure, supply chains and the physical systems essential for delivering care. This necessitates the strategic use of geospatial tools to guide the delivery of healthcare services and make evidence-informed priorities, especially in contexts with scarce human and financial resources. In this article, we highlight several published papers that have been used throughout the phases of the disaster management cycle in relation to health service delivery. We complement the findings from these publications with a rapid scoping review to present the body of knowledge for using spatial methods for health service delivery in the context of disasters. The main aim of this article is to demonstrate the benefits and discuss the challenges associated with the use of geospatial methods throughout the disaster management cycle. Our scoping review identified 48 articles employing geospatial techniques in the disaster management cycle. Most of them focused on geospatial tools employed for preparedness, anticipatory action and mitigation, particularly for targeted health service delivery. We note that while geospatial data analytics are effectively deployed throughout the different phases of disaster management, important challenges remain, such as ensuring timely availability of geospatial data during disasters, developing standardized and structured data formats, securing pre-disaster data for disaster preparedness, addressing gaps in health incidence data, reducing underreporting of cases and overcoming limitations in spatial and temporal coverage and granularity. Overall, existing and novel geospatial methods can bridge specific evidence gaps in all phases of the disaster management cycle. Improvement and 'operationalization' of these methods can provide opportunities for more evidence-informed decision making in responding to health crises during climate change.

Short-term starvation and refeeding in the greater amberjack (Seriola dumerili, Risso 1810): New insights on physiological and metabolic traits

The greater amberjack (Seriola dumerili) is a fish species with high commercial value and excellent growth rates, which has led to increasing interest in the aquaculture sector to optimize and expand its production. To gain a deeper insight into its physiological aspects, the present study aimed to evaluate the metabolic responses of S. dumerili juveniles after a short period of starvation and subsequent refeeding. For this purpose, the fish were fed ad libitum twice a day for four weeks and were then subjected to a starvation challenge followed by a refeeding period of seven days each. After one week of starvation, the fish showed weight loss but were able to adapt their metabolism by maintaining carbohydrate levels and using lipids and proteins as the main energy sources. All this was supported by decreased glycogenolytic potential during starvation, indicated by lower GP activity, and an increase in gluconeogenic pathway, as evidenced by the observed activity of FBP, LDH, and transaminases (ALT and AST). Moreover, the refeeding period stimulated growth processes by activating metabolic pathways, resulting in higher feed efficiency rates. Correspondingly, the highest plasma cortisol levels were observed during the refeeding period, stimulating anticipatory activity after starvation and contributing to metabolic recovery. In conclusion, S. dumerili can efficiently regulate its internal environment during a short period of starvation, improving feed efficiency and growth parameters after refeeding and rapidly restoring the initial metabolic state. These results could represent promising insights to enhance the feeding program for this species.

Impact-based forecasting of tropical cyclone-related human displacement to support anticipatory action

Tropical cyclones (TCs) displace millions every year. While TCs pose hardships and threaten lives, their negative impacts can be reduced by anticipatory actions like evacuation and humanitarian aid coordination. In addition to weather forecasts, impact forecast enables more effective response by providing richer information on the numbers and locations of people at risk of displacement. We introduce a fully open-source implementation of a globally consistent and regionally calibrated TC-related displacement forecast at low computational costs, combining meteorological forecast with population exposure and respective vulnerability. We present a case study of TC Yasa which hit Fiji in December 2020. We emphasise the importance of considering the uncertainties associated with hazard, exposure, and vulnerability in a global uncertainty analysis, which reveals a considerable spread of possible outcomes. Additionally, we perform a sensitivity analysis on all recorded TC displacement events from 2017 to 2020 to understand how the forecast outcomes depend on these uncertain inputs. Our findings suggest that for longer forecast lead times, decision-making should focus more on meteorological uncertainty, while greater emphasis should be placed on the vulnerability of the local community shortly before TC landfall. Our open-source codes and implementations are readily transferable to other users, hazards, and impact types.

More than Magnitude: Towards a multidimensional understanding of unprecedented weather to better support disaster management

The 1900 Galveston Texas Hurricane, the 2021 Pacific Northwest heatwave, and the 2023 Tropical Cyclone Freddy were all events that were unprecedented in diverse ways and had severe humanitarian impacts. Understanding past and future risk of unprecedented weather is an emerging question across climate science disciplines but use of this research by the humanitarian sector has been limited. This cross-disciplinary paper is an effort by climate scientists and humanitarian practitioners to address this gap. For it, we combined narrative and scoping literature reviews with structured practitioner engagement to develop a working definition and typology of unprecedented weather through a disaster management lens. We qualitatively coded over 400 peer-reviewed articles to highlight the current state of research on unprecedented weather, and then discussed these findings in a workshop with 48 humanitarian practitioners. Our results show that, while analyses of past and future unprecedented weather often focus on the magnitude of such events, extreme weather can be unprecedented in many other dimensions, all which have significant implications for early warning, anticipatory action, and disaster response planning. We conclude with a call for more imagination and diversity in research on extreme weather risks, and for closer collaboration between climate scientists and disaster managers to design and answer questions that matter for humanitarian outcomes.

The Biblical Aspect of the Term “Immaculate” as the Basis for the Dogma of the Immaculate Conception

In this article, we considered the biblical basis of the Church's teaching on the Immaculate Conception. The Catholic Church proceeds from the biblical concept of «kekharitomen» (гр.), which formed the basis of the teaching about this glorious Marian dogma. In this work, the linguistic sources of this term were presented, which shed light on the actions of the Holy Spirit, Who, erring over all creation with the will of the Father through the Son, being their common personal Love, creates Mary as a new Creation. In Eph 1, 6 St. Paul speaks of our justification through the will of the Father by grace in His beloved Son Jesus Christ. The Apostle of the Nations uses the verb «kharito» (гр.), which means that the Mother of God was justified in advance thanks to the anticipatory action of the Holy Spirit with the will of the Son and the Father. God would not be omnipotent if He did not protect His Mother from the sin of the ancestors. That is why, thanks to the future Incarnation, He forbids the Most Pure One from original sin in an anticipatory way, so that through Her He may take the body and redeem the whole world on the Cross. This work should become a bridge between Catholics, Orthodox and Protestants, who should look at Christ in a new way through the prism of Mary's perfect holiness from the Conception.

Multi-modal transformer with language modality distillation for early pedestrian action anticipation

Language-vision integration has become an increasingly popular research direction within the computer vision field. In recent years, there has been a growing recognition of the importance of incorporating linguistic information into visual tasks, particularly in domains such as action anticipation. This integration allows anticipation models to leverage textual descriptions to gain deeper contextual understanding, leading to more accurate predictions. In this work, we focus on pedestrian action anticipation, where the objective is the early prediction of pedestrians’ future actions in urban environments. Our method relies on a multi-modal transformer model that encodes past observations and produces predictions at different anticipation times, employing a learned mask technique to filter out redundancy in the observed frames. Instead of relying solely on visual cues extracted from images or videos, we explore the impact of integrating textual information in enriching the input modalities of our pedestrian action anticipation model. We investigate various techniques for generating descriptive captions corresponding to input images, aiming to enhance the anticipation performance. Evaluation results on available public benchmarks demonstrate the effectiveness of our method in improving the prediction performance at different anticipation times compared to previous works. Additionally, incorporating the language modality in our anticipation model proved significant improvement, reaching a 29.5% increase in the F1 score at 1-second anticipation and a 16.66% increase at 4-second anticipation. These results underscore the potential of language-vision integration in advancing pedestrian action anticipation in complex urban environments.

The struggle for an alternative future: Anticipatory actions and socio-environmental movements in Alt Empordà (Catalonia)

This article describes the environmental practices and discourses of local actors in the Alt Empordà region in Catalonia and how they contribute to the production of a vision of the environmental future. Based on a political ecology of hope framework, it presents different anticipatory actions and strategies which have created this vision of the environmental future since the 1970s, presenting key historic moments and actions which are central to various strategies which have been elaborated in order to secure access to the land and its resources. For decades, different successful campaigns have nourished a particular outlook, aimed at creating an environmental future which is accessible, attainable, and pragmatic. The article then analyzes how recent environmental changes and the consequences of these changes for the different local environments are significantly changing the actors’ view of the environmental future. Different shades of darker futures are emerging, in which the clear gains from environmental struggles are less tangible. It is hoped that in these futures access to nature will be more inclusive and less oriented towards a modernist and developmentalist approach to the territory. This perspective favours a thriving nature, but does so by rethinking conventional conservation models in order to take into account the changing nature of the environment in a climate change context. It is hoped that the region's demographic structure will enable its inhabitants to maintain, occupy and live on the territory, rather than leaving it to consist of protected areas or tourist areas only, and to build a different relationship with energy and water consumption. Finally, we discuss how these transformations modify local perceptions of the future and may foster an “anticipated solastalgia” as an emerging category of environmental thought.

Possibilities and limitations of anticipatory action in complex crises: acting in advance of flooding in South Sudan.

Little evidence exists on the design and implementation of anticipatory action (AA) in complex crises. This article examines a 2022 United Nations 'early action' pilot in South Sudan in advance of extreme flooding. As a case study of efforts to act in a complex crisis, it contributes to learning on assisting conflict-affected and displaced populations in advance of extreme weather events. The research points towards the possibility and value of implementing forecast-informed early action in complex contexts with limited forecast skills and multiple hazards when trigger-based AA is not possible. It also argues for the need to link AA implemented in complex crises to development and peacebuilding actors and processes. More broadly, examining the perceptions and processes of AA in the form of early action, rather than a formal AA framework, is highly relevant for the AA community as attention is increasingly placed on providing AA in countries affected by fragility and conflict.

Determinants of Information Needs on Climate-Smart Agriculture Among Male and Female Farmers Across Farming Systems and Agroecological Zones in Sierra Leone: Implications for Anticipatory Actions

Determinants of Information Needs on Climate-Smart Agriculture Among Male and Female Farmers Across Farming Systems and Agroecological Zones in Sierra Leone: Implications for Anticipatory Actions

From Recognition to Prediction: Leveraging Sequence Reasoning for Action Anticipation

The action anticipation task refers to predicting what action will happen based on observed videos, which requires the model to have a strong ability to summarize the present and then reason about the future. Experience and common sense suggest that there is a significant correlation between different actions, which provides valuable prior knowledge for the action anticipation task. However, previous methods have not effectively modeled this underlying statistical relationship. To address this issue, we propose a novel end-to-end video modeling architecture that utilizes attention mechanisms, named Anticipation via Recognition and Reasoning (ARR). ARR decomposes the action anticipation task into action recognition and sequence reasoning tasks, and effectively learns the statistical relationship between actions by next action prediction (NAP). In comparison to existing temporal aggregation strategies, ARR is able to extract more effective features from observable videos to make more reasonable predictions. In addition, to address the challenge of relationship modeling that requires extensive training data, we propose an innovative approach for the unsupervised pre-training of the decoder, which leverages the inherent temporal dynamics of video to enhance the reasoning capabilities of the network. Extensive experiments on the Epic-kitchen-100, EGTEA Gaze+, and 50salads datasets demonstrate the efficacy of the proposed methods. The code is available at https://github.com/linuxsino/ARR .

Preseason maize and wheat yield forecasts for early warning of crop failure

Provided the considerable logistical challenges of anticipatory action and disaster response programs, there is a need for early warning of crop failures at lead times of six to twelve months. But crop yield forecasts at these lead times are virtually nonexistent. By leveraging recent advances in climate forecasting, we demonstrate that global preseason crop yield forecasts are not only possible but are skillful over considerable portions of cropland. Globally, maize and wheat forecasts are skillful at lead times of up to a year ahead of harvest for 15% and 30% of harvested areas, respectively. Forecasts are most skillful in Southeast Africa and Southeast Asia for maize and parts of South and Central Asia, Australia, and Southeast South America for wheat. Wheat forecasts, furthermore, remain skillful at lead times of over 18 months ahead of harvest in some locations. Our results demonstrate that the potential for preseason crop yield forecasts is greater than previously appreciated.

Brain signatures of catastrophic events: Emotion, salience, and cognitive control.

Anticipatory brain activity makes it possible to predict the occurrence of expected situations. However, events such as traffic accidents are statistically unpredictable and can generate catastrophic consequences. This study investigates the brain activity and effective connectivity associated with anticipating and processing such unexpected, unavoidable accidents. We asked 161 participants to ride a motorcycle simulator while recording their electroencephalographic activity. Of these, 90 participants experienced at least one accident while driving. We conducted both within-subjects and between-subjects comparisons. During the pre-accident period, the right inferior parietal lobe (IPL), left anterior cingulate cortex (ACC), and right insula showed higher activity in the accident condition. In the post-accident period, the bilateral orbitofrontal cortex, right IPL, bilateral ACC, and middle and superior frontal gyrus also showed increased activity in the accident condition. We observed greater effective connectivity within the nodes of the limbic network (LN) and between the nodes of the attentional networks in the pre-accident period. In the post-accident period, we also observed greater effective connectivity between networks, from the ventral attention network (VAN) to the somatomotor network and from nodes in the visual network, VAN, and default mode network to nodes in the frontoparietal network, LN, and attentional networks. This suggests that activating salience-related processes and emotional processing allows the anticipation of accidents. Once an accident has occurred, integration and valuation of the new information takes place, and control processes are initiated to adapt behavior to the new demands of the environment.

Managing compound events in the COVID-19 era: A critical analysis of gaps, measures taken, and challenges

Since the beginning of 2020, the focus has predominantly been on the prevention and control of the COVID-19 pandemic. However, the 'new normal' has drastically altered responses to other hazards, underscoring the need to prepare and plan for complex, multi-risk scenarios. Nevertheless, there's limited literature on disaster preparedness and response for compound hazard scenarios, particularly pandemic-natural hazard hybrids. The few studies that have explored concurrent hazards during the COVID-19 pandemic have largely focused on developed, high-income countries. Yet, compound events can exacerbate pre-existing socio-economic vulnerabilities in developing countries, straining systems and causing disproportionate impacts. This study addresses this lacuna by examining (1) emergency preparedness and response measures for Sri Lanka's Southwest and Northeast monsoons during the COVID-19 pandemic, and (2) the feasibility of current Disaster Risk Reduction (DRR) strategies for managing compound pandemic-natural hazard events in Sri Lanka. The research involves a desk-based policy analysis of disaster and health policies, key informant interviews with 26 officials, and focus group discussions with Public Health Inspectors from Ratnapura, Jaffna, Matara, and Gampaha. Findings indicate that while significant steps were taken, including impact scenario analyses, stakeholder meetings, and revised response measures, challenges such as resource constraints and limited capacity persisted. This study further demonstrates that the country's existing DRR strategies are characterised by gaps that may hinder capacities to effectively manage compound hazards. The findings of this study therefore encourage the implementation of structural changes and anticipatory action that adequately account for the complexity of risk supported by cross-sectoral collaboration. The study particularly highlights the importance of proactive scenario-based planning, developing composite risk matrices that account for multiple dimensions of vulnerability, fostering cross-sectoral and cross-level collaboration, and adapting existing early warning systems to effectively manage compound hazard scenarios.

Causal discovery reveals complex patterns of drought-induced displacement

The increasing frequency and severity of droughts present a significant risk to vulnerable regions of the globe, potentially leading to substantial human displacement in extreme situations. Drought-induced displacement is a complex and multifaceted issue that can perpetuate cycles of poverty, exacerbate food and water scarcity, and reinforce socio-economic inequalities. However, our understanding of human mobility in drought scenarios is currently limited, inhibiting accurate predictions and effective policy responses. Drought-induced displacement is driven by numerous factors and identifying its key drivers, causal-effect lags, and consequential effects is often challenging, typically relying on mechanistic models and qualitative assumptions. This paper presents a novel, data-driven methodology, grounded in causal discovery, to retrieve the drivers of drought-induced displacement within Somalia from 2016 to 2023. Our model exposes the intertwined vulnerabilities and the leading times that connect drought impacts, water and food security systems along with episodes of violent conflict, emphasizing that causal mechanisms change across districts. These findings pave the way for the development of algorithms with the ability to learn from human mobility data, enhancing anticipatory action, policy formulation, and humanitarian aid.

A coactivation strategy in anticipatory postural adjustments during voluntary unilateral arm movement while standing in individuals with bilateral spastic cerebral palsy

Individuals with bilateral spastic cerebral palsy (BSCP) reportedly has problems with anticipatory postural adjustments (APAs) while standing. However, the use of coactivation strategy in APAs in individuals with BSCP has conflicting evidence. Hence, this study aimed to investigate postural muscle activities in BSCP during unilateral arm flexion task in which postural perturbations occur in the sagittal, frontal, and horizontal planes. We included 10 individuals with BSCP with level II on the Gross Motor Function Classification System (BSCP group) and 10 individuals without disability (control group). The participants stood on a force platform and rapidly flexed a shoulder from 0° to 90° at their own timing. Surface electromyograms were recorded from the rectus femoris, medial hamstring, tibialis anterior, and medial gastrocnemius. The control group showed a mixture of anticipatory activation and inhibition of postural muscles, whereas the BSCP group predominantly exhibited anticipatory activation with slight anticipatory inhibition. Compared with the control group, the BSCP group tended to activate the ipsilateral and contralateral postural muscles and the agonist–antagonist muscle pairs. The BSCP group had a larger disturbance in postural equilibrium, quantified by the peak displacement of center of pressure during the unilateral arm flexion, than those without disability. Individuals with BSCP may use coactivation strategy, mainly the anticipatory activation of postural muscle activity, during a task that requires a selective postural muscle activity to maintain stable posture.

Pengukuran Kinerja Mesin Sewage Treatment Plant (STP) menggunakan Metode Overall Equipment Efeectiveness

One of the important things in supporting the operational management of the building which is part of the building's mechanical and electrical aspects is the building's domestic liquid waste management system. The availability of liquid waste production cannot be separated from the performance of the Sewage Treatment Plan (STP) machine as a work tool that produces domestic liquid waste production in HDI Hive Menteng Building. STP machine maintenance is one of the important factors in ensuring the quality of liquid waste production in accordance with the Environmental Service (DLH) quality standards. The machine maintenance system can be used as a benchmark for measuring the effectiveness of STP machines. Machine performance with a high rhythm without stopping 24 hours which continues to increase and is comparable to the tenant accoupancy level, of course requires anticipatory action for machine breakdown problems. Overall Equipment Effectiveness (OEE) is a method used to measure and maximize machine effectiveness based on three categories, namely Availability rate, Performance rate, Quality rate. From the results of calculating the OEE value for the STP machine at PT. Gaia Care International) of 82.96%, in general the OEE value is still below the JIPM standard value, namely greater than 85%. For this reason, in this research, an analysis was carried out using a fishbone diagram, which is used to determine the causes and effects of not achieving the effectiveness of the STP machine. Based on the results of the Fish Bone Diagram analysis with several factors including; Machines, methods, people and materials can be concluded as factors that cause a decrease in the OEE value of STP machines, namely rework losses which are quite high so that they affect the Quality OEE value in STP machine measurements. The proposal given to increase the effectiveness of the STP Machine for controlling Rework Losses is to improve the general cleaning routine and increase the Backwash Pump Capacity in the aeration tank and Inlet.

The impact of forage condition on household food security in northern Kenya and southern Ethiopia

We studied the causal link between forage condition and food security in northern Kenya and southern Ethiopia and probed the mechanisms through which the effects occur. The study utilized weekly panel survey data collected over a period of 94 weeks (March 2021–December 2022) from drought-prone pastoral regions and estimated two-stage least squares instrumental variables regression to assess causal impacts. We found that an increase in the normalized difference vegetation index (NDVI) – our proxy for forage condition – by 10% significantly reduced the likelihood of a household experiencing food insecurity by 12 percentage points. The main mechanisms through which improved forage condition increased food security were livestock productivity enhancement, reduced prices of basic food commodities (cereals, legumes, and vegetables), and utilization of better coping strategies by households. These findings suggest the need for interventions and initiatives aimed at boosting livestock productivity and reducing vulnerability to drought-induced poor forage conditions. Efforts focused on the monitoring of forage conditions and supporting market development for livestock feed are important for addressing the adverse impacts of drought and deteriorating forage conditions in the horn of Africa. Furthermore, the monitoring of drought conditions and shocks using high-frequency data has the potential for providing early warning and informing anticipatory action.

Agricultural Drought-Triggering for Anticipatory Action in Papua New Guinea

Throughout its history, Papua New Guinea (PNG) has faced recurrent agricultural droughts, imposing considerable strain on both livelihoods and the economy. Particularly severe droughts have been associated with El Niño climate patterns. During these episodes, PNG becomes especially vulnerable to extended periods of aridity and diminished precipitation. Historically, humanitarian assistance for these events has primarily focused on responding to emergencies after an agricultural drought has been declared and communities have already been impacted. Here, we developed a proactive agricultural drought-triggering method for anticipatory action (AA) in PNG to offer a more sustainable and cost-effective approach to address this hazard. Our AA uses weather forecasts and risk data to identify and implement mitigative actions before a disaster occurs. The research details a step-by-step guide from early warning to action implemented by the Food and Agricultural Organization of the United Nations and the Government of Papua New Guinea. This preemptive disaster risk management initiative integrates a combined drought index (CDI) with specific thresholds and tailored anticipatory actions based on crop calendars. Moreover, the developed CDI provides a 3-month lead time for implementing AA to reduce the impact of the agricultural drought. During the El Niño-induced drought event that began in 2023, the CDI was tested and the AA was piloted for the first time.

Anticipate, automate, accelerate: A framework for blockchain in anticipatory action

Anticipatory action (AA) refers to a series of interventions executed when a hazard presents imminent danger, as indicated by forecasts, early warnings, or pre-disaster risk analyses. It involves proactive measures undertaken by individuals or organizations before an expected disaster to lessen its impact on people, assets, and infrastructure likely to be affected. An increasing array of inter-governmental bodies, governments, United Nations entities, global non-governmental organizations, and Red Cross and Red Crescent movements, have been delving into and utilizing anticipatory action. It has been heralded globally by governments, donors and organizations alike as being a more protective, practical, and cost-effective method of disaster risk management. However, ongoing pilots by the Food and Agriculture Organization of the United Nations (FAO) and partners have exposed a significant level of friction between the capacity and speed of existing systems and stakeholders to execute these processes in a manner that is synchronized and timely enough to have the intended impact.This research investigates the potential of blockchain technology to streamline AA programs. It analyzes existing academic and non-academic sources (scholarly and gray literature) to develop a framework for identifying which blockchain applications can be most beneficial. By viewing smart contracts, data oracles, and decentralized finance (DeFi) as a toolbox, the framework examines how these technologies can be used to digitize and simplify AA processes. For AA practitioners, this approach offers a practical solution to potentially increase efficiency and speed within existing program structures. Additionally, it may have the secondary benefit of simplifying cash transfer delivery. Ultimately, this piece seeks to be more thought-provoking than prescriptive; as both areas of work (AA and blockchain technology) are continuously evolving, hopefully new cross-sectoral collaborations can ensue and provide more granular insights on what AA and blockchain synergies look like in practice.