Economy of Things Solutions USA Unlock Asset Intelligence for Maximum ROI
The Economy of Things solutions USA creates a secure digital marketplace where everyday devices, from smart meters to industrial sensors, can autonomously buy, sell, and trade their own data and services. This machine-to-machine economy allows your connected car to pay for its own charging session or a factory robot to lease out its idle processing power, unlocking new revenue streams from assets you already own. By enabling trustless microtransactions directly between devices, it turns static infrastructure into an active, self-managing network that saves you money and optimizes operations without manual oversight.
Defining the Economic Model for a Connected World
In the USA, the economic model for a connected world under Economy of Things solutions shifts value from selling a device to monetizing its real-time data streams. For instance, a city-owned sensor network doesn’t just measure traffic; it sells that flow data to logistics firms optimizing delivery routes. Each sensor becomes a micro-transaction node. The model hinges on micropayment ledgers processing fractions of a cent per data request, enabling a farmer to pay a drone network for a single field scan. This turns idle infrastructure into a revenue asset, where a connected parking meter earns more from aggregated movement analytics than from parking fees alone.
How Smart Infrastructure Creates New Revenue Streams
Smart infrastructure transforms passive assets into active revenue generators through data-driven monetization models. Streetlights equipped with sensors sell parking occupancy data to navigation apps, while smart grids enable dynamic pricing for energy storage. A building’s IoT network can lease its excess bandwidth to nearby devices. Usage-based billing turns infrastructure maintenance contracts into recurring income streams, as predictive analytics automate service fees. Q: How do smart roads create revenue? By embedding weigh-in-motion sensors, roads capture vehicle load data and charge logistics companies per ton-mile, eliminating manual toll booths. Every connected object becomes a transactional node.
Tokenizing Real-World Assets for Data and Utility
Tokenizing real-world assets essentially turns physical stuff, like a parking space or a home battery, into digital tokens that hold both data and utility. You own the token, so you own a share of that asset’s function. For instance, your electric vehicle’s battery could be tokenized, letting its stored energy trade on local grids while you track its charge cycle data. This moves beyond simple ownership; your token unlocks direct control over the asset’s programmable data rights and usage, allowing you to sell its capabilities or grant access without a middleman. It’s about making your assets work smartly and flexibly for you.
Key Drivers: 5G, Edge Computing, and Digital Twins
5G, edge computing, and digital twins form the operational backbone of the Economy of Things. 5G delivers the ultra-low latency and high bandwidth needed for real-time asset interactions, while edge computing processes data locally to eliminate cloud round-trips, enabling sub-millisecond decision cycles for autonomous payments and device coordination. Digital twins provide a synchronized virtual replica of physical assets, allowing dynamic pricing and predictive maintenance without interrupting live operations. Together, they shift economic logic from simple connectivity to real-time, trustless value exchange between machines.
5G provides the speed, edge computing ensures instant processing, and digital twins enable continuous simulation—each driver is essential for executing automated transactions in the Economy of Things.
Core Sectors Adopting Automated Value Exchange
In the USA, energy and logistics sectors are the primary engines adopting automated value exchange within Economy of Things solutions. A solar array on a commercial building in Texas, for instance, now automatically negotiates and sells excess kilowatts to a neighboring EV charging hub without human intervention—each transaction settled instantly via digital wallets embedded in the meters. Similarly, a cold-chain logistics firm in California uses sensor-equipped pallets that autonomously pay tolls, parking fees, and recharging costs as they move through port yards. These micro-transactions flow seamlessly between machines, eliminating billing delays and manual reconciliation. The result is a self-operating economic layer where trucks, grids, and storage units trade energy and access rights in real-time, reducing operational friction and unlocking value from idle assets.
Energy Grids and Peer-to-Peer Power Trading
Within the USA’s Economy of Things, energy grids transform through automated peer-to-peer power trading, enabling microgrids to dynamically reroute surplus solar energy between homes. Your smart meter negotiates directly with a neighbor’s electric vehicle charger, balancing local loads without central utility delay. Trading flows adapt in real-time to a household’s charging event, preventing grid strain by shifting power to a commercial battery bank two blocks away. Settlement occurs via smart contracts as electrons physically travel—no manual billing steps are involved. This automates value exchange at the distribution edge, turning every solar panel into an autonomous energy merchant within the local grid ecosystem.
Smart Cities: Monetizing Traffic, Parking, and Waste Data
Smart cities in the USA convert urban flow into revenue by treating traffic, parking, and waste data as tradable assets. Real-time congestion data from vehicle sensors is sold to logistics firms for dynamic routing, reducing fuel costs. Parking occupancy metrics enable variable pricing where drivers bid for spots, maximizing lot revenue. Waste bin fill-levels trigger automated value exchange with haulers, charging only for actual collection. This creates a direct cycle:
- sensors capture usage data,
- smart contracts price and sell that data,
- proceeds fund city infrastructure.
Residents benefit from fewer traffic jams, lower parking fees, and optimized waste pickup schedules.
Supply Chain and Logistics with Real-Time Asset Tracking
In the USA, Economy of Things solutions are revolutionizing supply chain and logistics by enabling real-time asset tracking. Instead of relying on periodic scans, pallets and containers now broadcast their precise location and condition autonomously. This removes blind spots during transit, allowing you to verify that temperature-sensitive goods stayed within range or that a critical package didn’t get rerouted. It effectively turns every high-value asset into a live data node within your operational network. You get instant alerts for delays or mishandling without manual check-ins. This granular visibility cuts inventory loss and streamlines dock scheduling, making daily logistics less reactive.
Q: How does real-time asset tracking prevent theft in transit?
A: Sensors trigger an immediate alert if a sealed container is opened at an unauthorized location or time, allowing security teams to react before the asset leaves the logistical loop.
The result is a tighter, more transparent flow where automated value exchange happens directly between assets and infrastructure.
Market Leaders and Emerging Platforms in the United States
Helium and Streamr are market leaders in the US, offering decentralized networks where your vehicle or device can earn crypto for sharing sensor data or connectivity. These platforms provide a direct alternative to centralized giants, giving users control over their economic assets.Which emerging platform is fastest to integrate for a US user? The answer is Dimo, a telematics protocol, as it plugs directly into your car’s OBD-II port within minutes, instantly enabling you to monetize anonymized driving data. Emerging platforms like Nodle on your smartphone and Hivemapper with dashcams are also viable, each targeting specific verticals—phone-based asset tracking and real-time mapping, respectively—rather than a one-size-fits-all approach.
Major Tech Players Shaping the Infrastructure
In the United States, major tech players are constructing the foundational layers for Economy of Things (EoT) solutions. Amazon Web Services (AWS) provides a critical backbone with its IoT Core and edge computing capabilities, enabling devices to process data locally before transmission. Google Cloud focuses on data unification and AI-driven analytics for connected assets, while Microsoft Azure leverages its enterprise mesh to integrate physical devices with existing business software. These companies compete on latency reduction and seamless device-ecosystem compatibility, forming the essential infrastructure upon which EoT applications operate.
- AWS offers a scalable IoT Core for device management and secure data ingestion.
- Google Cloud provides AI models that process data from connected assets in real time.
- Microsoft Azure integrates device telemetry directly into Dynamics 365 and Power Platform.
Startups Pioneering Micro-Transactions and Data Marketplaces
In the USA, startups like Streamr and IOTA-based ventures enable Economy of Things (EoT) data marketplaces where IoT devices directly sell sensor readings to buyers via automated, low-fee micro-transactions. These platforms use decentralized ledgers to authorize real-time payments for discrete data parcels, such as traffic flow from a smart vehicle or energy usage from a connected appliance. Users configure their devices to stream data in exchange for crypto-tokens without human intervention. Some ventures offer aggregated subscription tiers for industrial sensors.
Startups pioneer micro-transaction frameworks and peer-to-peer data marketplaces, allowing IoT devices to autonomously trade small data packets for tokenized payments.
Partnerships Between Telecoms and Cloud Providers
In the U.S., partnerships between telecoms and cloud providers directly accelerate Economy of Things (EoT) deployment by fusing network edge infrastructure with scalable compute. For example, AT&T’s alignment with Microsoft Azure allows sensor data processing locally on 5G towers, slashing latency for autonomous fleet management. Similarly, T-Mobile collaborates with Amazon Web Services (AWS) Wavelength to embed cloud instances at cell sites, enabling real-time logistics tracking without backhaul delays. These collaborations eliminate the need for businesses to build proprietary middleware; instead, they access pre-integrated connectivity and analytics as a single service.
What do these partnerships mean for an IoT business? They mean you can deploy a connected device that processes data directly within a carrier’s physical network, reducing round-trip times from seconds to milliseconds without licensing complex cloud subscriptions.
Regulatory Landscape and Compliance Considerations
In deploying Economy of Things solutions in the USA, regulatory compliance hinges on spectrum allocation for machine-to-machine communications, typically requiring unlicensed ISM band usage governed by FCC Part 15 rules. You must also navigate state-specific data privacy laws, such as the CCPA in California, which mandate explicit consent before collecting telemetry data from connected assets. A practical priority is ensuring your device firmware supports over-the-air updates to address evolving security protocols from NIST. Q: How do I handle cross-state compliance? A: Implement a dynamic policy engine that flags data based on the asset’s physical location at the moment of transmission, applying the strictest state requirements by default. This prevents liability when a device roams across states like Texas or New York.
Data Privacy Laws Impacting Sensor-Generated Revenue
Stringent data privacy laws directly constrain how sensor-derived data can be monetized, impacting revenue models for Economy of Things solutions. Regulations often require explicit consent before collecting personal identifiers from sensors, limiting the granularity of behavioral insights sold to third parties. This restriction forces providers to shift revenue strategies from selling raw data to offering anonymized, aggregated analytics that still hold commercial value. The cost of implementing privacy-by-design architectures, such as edge processing to avoid data transmission, reduces profit margins unless offset by premium pricing for privacy-compliant sensor analytics. Compliance also dictates data retention schedules; prematurely purged data erases archival revenue opportunities from longitudinal insights.
FCC and Spectrum Management for Machine-to-Machine Payments
The FCC’s spectrum management directly impacts machine-to-machine (M2M) payments by designating specific unlicensed and licensed bands (e.g., the 900 MHz ISM band for long-range, low-power devices) to ensure reliable, low-latency transaction data transmission. This allocation prevents interference between competing M2M payment terminals and critical infrastructure, supporting secure, real-time payment authorizations in the Economy of Things. EoT spectrum compliance requires devices to operate within defined power limits and stay within assigned frequencies to avoid service degradation, a key practical consideration for payment system deployment.
- Use only FCC-approved frequency bands to guarantee payment signal integrity in dense device environments.
- Configure M2M payment devices to automatically switch channels to avoid interference from other unlicensed-band users.
- Verify that device firmware adheres to FCC spectrum access rules to maintain transaction reliability during peak usage.
State-Level Incentives for Automated IoT Commerce
Several states now offer targeted tax breaks for automated IoT commerce, like reduced property tax on autonomous delivery robots or server farms. You might find grants for deploying smart vending systems in rural zones to improve access. These perks often require proving the automation directly boosts local supply chain efficiency. Focus on state-level tax credits for edge computing infrastructure to lower upfront costs for your Economy of Things setup. Check each state’s economic development office for specific automation thresholds.
State-level incentives for automated IoT commerce cut costs on hardware and data processing, but only when your system demonstrably enhances local logistics or retail reach.
Technical Backbone for Automated Financial Flows
The Technical Backbone for Automated Financial Flows within USA-based Economy of Things solutions relies on decentralized ledger networks and smart contract protocols to execute micropayments instantly between devices. When a connected vehicle in a US smart city pays a charging station for electricity, or an industrial sensor compensates a drone for data delivery, the backbone processes these transactions without human intervention. This system uses tokenization to convert usage metrics into verifiable digital assets, enabling trustless settlement across IoT devices. By embedding payment logic directly into device firmware, the backbone eliminates latency and reconciliation overhead, allowing physical assets to autonomously generate and distribute value in real-time. The result is a frictionless economic layer where machines negotiate and transact based on predefined, auditable rules. This framework is essential for scaling automated commerce across distributed, device-heavy networks in the US.
Distributed Ledgers and Smart Contracts for Trustless Settlements
In Economy of Things (EoT) solutions within the USA, distributed ledgers record device-to-device transactions immutably, while smart contracts automate settlement logic without intermediaries. A digital twin of an asset triggers a smart contract upon service completion, issuing a tokenized payment directly to the machine’s wallet. This eliminates reconciliation latency and chargeback risk, enabling trustless micro-settlements between vehicles, sensors, and charging stations. The ledger’s consensus mechanism verifies data integrity before any transfer executes.
- Smart contracts enforce payment release only after IoT sensor data confirms delivery or performance.
- Distributed ledger prevents double-spending or duplicate invoices across heterogeneous device networks.
- Multi-signature conditions in contracts split value across multiple parties in a single settlement action.
Interoperability Standards Across Different IoT Networks
Interoperability standards across different IoT networks form the technical glue for automated financial flows in the Economy of Things USA. Protocols like Matter and oneM2M allow a smart vehicle from one manufacturer to transact with a roadside charger from another, using a unified data schema without manual pairing. OCF and LwM2M standards further ensure that sensor readings from disparate agricultural IoT networks trigger immediate, trustless micropayments for water usage. Without these cross-network standards, a connected device fleet cannot execute multi-party settlements—each sensor or actuator must speak the same transactional language for automated ledger entries to occur in real time.
Interoperability standards across different IoT networks eliminate protocol silos, enabling any certified device to initiate or receive a financial transaction within the Economy of Things without human intervention or custom integration.
Security Protocols to Prevent Fraud in Device-Driven Payments
Device-driven payments within Economy of Things solutions USA rely on layered cryptographic authentication to prevent fraud. Each transaction uses device-specific rotating tokens that are validated against a hardware-backed secure element, making unauthorized cloning or replay attacks computationally infeasible. Behavioral biometrics continuously verify the user’s interaction pattern with the device, while geo-fencing and temporal signing ensure payment authorization occurs only within predefined physical and time boundaries. End-to-end encryption isolates each payment payload from other device communications, and mutual TLS (mTLS) requires both the device and the payment gateway to prove their identities before any value transfer is approved.
- Device-specific rotating tokens stored in secure hardware enclaves prevent token replay and cloning.
- Behavioral biometrics (e.g., tap cadence, acceleration profile) authenticate the user during the payment action.
- Geo-fencing with temporal signing rejects authorization attempts outside approved spatial-temporal windows.
- Mutual TLS (mTLS) enforces bidirectional identity verification between device and payment gateway.
Use Cases Transforming American Households and Industries
In a Dallas suburb, an Economy of Things solution turns a home’s electric vehicle charger into an earning asset, automatically selling surplus power back to a local factory during peak demand. Across the Rust Belt, a manufacturing plant uses smart sensor mesh networks to bid its idle machine tools into a regional capacity marketplace, reducing downtime costs by 40%. This same network unlocks your household refrigerator to negotiate energy pricing every fifteen minutes, shaving monthly bills while stabilizing the grid. Meanwhile, a construction fleet in Phoenix shares live data on crane utilization, letting a nearby hotel lease idle hours for its renovation. These are not abstractions—they are real payments flowing between property and production, turning every appliance and assembly line into a self-optimizing economic node.
Connected Vehicles Paying for Charging, Tolls, and Parking
Instead of fumbling for cards or apps, your car handles payments for charging, tolls, and parking automatically. As you pull into an EV charger, the vehicle authorizes the session and deducts the cost from your linked wallet. On the highway, it pays tolls without you slowing down. When you park, the car settles the fee and can even extend time remotely. This creates a seamless, cashless drive where your vehicle acts as your payment proxy. Connected vehicle payments mean you never have to queue at a kiosk or hunt for change again.
Your car pays for charging, tolls, and parking on the go so you never stop to handle payments.
Smart Appliances Reordering Supplies and Billing Automatically
Smart appliances in USA homes now handle replenishment and payment without any effort on your part. A washer detects low detergent and orders more directly from your preferred retailer, billing the purchase to your linked account. The refrigerator tracks milk and eggs, adding them to a digital cart that finalizes payment on delivery day. This automated supply replenishment system removes the chore of inventory checks and checkout lines, seamlessly integrating spending into your routine.
Smart appliances handle the shopping and billing completely on their own.
Industrial Sensors Leasing Capacity and Selling Predictive Data
Industrial sensors leased by manufacturers convert factory floor capacity into a revenue stream, selling predictive data to insurers and supply chain partners. This sensor-driven predictive servicing model allows a plant to lease vibration or thermal sensors, then sell anomaly alerts to maintenance firms before breakdowns occur. The data itself becomes the product, not the machinery. The sequence operates as:
- Manufacturers install leased IoT sensors on critical equipment.
- Data streams are aggregated and analyzed for failure patterns.
- Predictive insights are sold to third parties for proactive intervention.
This direct swap of idle capacity for actionable intelligence keeps American industrial assets continuously monetized without upfront sensor ownership.
Monetization Strategies for Device Networks
Monetization of device networks within Economy of Things solutions in the USA hinges on turning data streams into direct revenue. Operators deploy micro-transaction billing for device-to-device services, such as smart appliances paying for energy optimization or autonomous vehicles purchasing parking rights. A key model is value-based pricing for sensor data, where a logistics network charges per verified tracking event.
The most effective strategy is creating “data dividends,” where device owners earn a fractional payment when their IoT asset’s performance data is sold to third-party analytics firms.
This transforms idle network capacity into a continuous, passive income stream for users and operators alike, without heavy upfront subscription costs.
Subscription Models vs. Pay-Per-Use for IoT Services
Choosing between subscription models and pay-per-use for IoT services hinges on user behavior predictability. A subscription offers stable, recurring access to device networks, ideal for constant monitoring or automated control, providing predictable costs. Conversely, pay-per-use aligns expenses directly with actual consumption, suiting sporadic data transmission or on-demand asset tracking. This eliminates waste from idle capacity, making it a cost-flexible IoT billing strategy for dynamic operations.
- Subscriptions secure continuous network availability for always-on devices like environmental sensors.
- Pay-per-use avoids upfront commitments, charging only per data packet or connection minute.
- Hybrid models mix a base subscription with usage surcharges for peak loads.
- Pay-per-use is practical for seasonal equipment monitoring where active periods are narrow.
Data Licensing and Aggregation from Distributed Sensors
For device networks in the USA, data licensing from distributed sensors turns raw environmental readings into recurring revenue by selling anonymized access to third-party buyers. Aggregation combines sensor streams—like traffic flow, air quality, or energy usage—into a clean, standardized feed that buyers actually want to use. You set the granularity; a city might pay more for street-level humidity readings than neighborhood averages. How do you prevent a single sensor owner from double-selling the same data to competing buyers? You use a blockchain ledger to timestamp every aggregated dataset, ensuring each licensed package has a unique fingerprint and traceable owner.
Dynamic Pricing Based on Real-Time Demand and Supply
In the Economy of Things, dynamic pricing based on real-time demand and supply allows device networks to automatically adjust service costs as usage fluctuates. A fleet of autonomous sensors, for instance, can increase data transmission fees during peak congestion to discourage non-critical loads, then drop rates when network capacity is idle. This creates a self-balancing system where high-demand devices effectively subsidize lower network costs for late-hour users. The core mechanism relies on smart contracts that read current grid or bandwidth strain, instantly recalibrating per-kilobyte or per-watt charges. This real-time pricing feedback loop ensures that each device only pays the economic value of its immediate network slice, eliminating flat-rate inefficiency.
Challenges to Scaling a Device-Driven Economy
Scaling a device-driven Economy of Things in the USA faces the core challenge of fragmented interoperability across proprietary device protocols, forcing users to build costly custom middleware for each new asset class. A key Q&A here: *Q: What practical hurdle kills most scaling efforts?* *A: The inability to standardize edge processing power—low-cost sensors lack the hardware to run unified security or data-aggregation code, creating a two-tier system where high-value devices integrate seamlessly while cheaper ones create integration debt.* Without a lean, cross-vendor abstraction layer for device identity and data flow, expansion stalls as operational complexity outpaces the marginal value each new connected device provides.
High Initial Deployment Costs for Hardware and Connectivity
High initial deployment costs for hardware and connectivity create a significant barrier for U.S. businesses adopting Economy of Things solutions. Procuring specialized sensors, gateways, and ruggedized edge devices for industrial or agricultural environments carries substantial upfront capital expenditure. Additionally, securing reliable, low-latency cellular or satellite connectivity across sprawling U.S. geographies requires expensive long-term contracts and infrastructure installation. These combined hardware and connectivity investments often delay or prevent pilot projects from scaling to full network coverage. Mitigation requires careful vendor selection and phased deployment strategies.
- Industrial-grade sensor arrays for each asset can cost thousands of dollars per unit
- Custom installation of wired or mesh network nodes adds labor and integration expenses
- Monthly connectivity fees for private LTE or LoRaWAN coverage increase total cost of ownership
- Redundant failover systems for critical applications double hardware procurement needs
Lack of Standardized Value Exchange Protocols
A core barrier is the absence of universal value exchange protocols, meaning devices from different manufacturers cannot agree on how to transact. Without standardized rules for pricing, payment triggers, or data ownership, a smart HVAC and a solar inverter, for example, cannot automatically negotiate energy credits. This forces users into closed ecosystems, where every new device requires manual integration or custom middleware. Practical scalability fails because each transaction pair must be individually programmed, eliminating the frictionless, automated exchange the Economy of Things promises.
The lack of standardized value exchange protocols creates interoperability silos, preventing automated, cross-platform device transactions without bespoke programming.
Consumer Trust and Transparency in Automated Transactions
In automated transactions within Economy of Things solutions in the USA, consumer trust hinges on verifiable transaction logs that prove device actions match authorized intent. Transparency requires that a smart contract’s logic—detailing payment triggers, data usage, and liability—be accessible to the user in plain language before consent. Without immutable, user-controlled records of each machine-to-machine payment, consumers cannot audit whether their vehicle or appliance agreed to a correct price. This lack of visibility erodes confidence, making adoption stall as users fear hidden fees or unauthorized device decisions. Practical design must embed clear, per-transaction receipts within the device’s own interface.
Future Outlook for Autonomous Marketplaces
The future outlook for autonomous marketplaces within the USA’s Economy of Things hinges on devices negotiating and transacting without human input. We’ll see smart appliances directly restocking supplies, and EVs paying chargers from their own digital wallets. The key challenge is establishing trustless device-to-device payments that are both instant and secure. If solved, your car could bid for the best parking spot while you sleep. Q: Will I need to approve every transaction my device makes? A: No—future systems will automate micro-payments under preset budgets, letting you simply review a summary instead.
Predicted Growth of Machine-to-Machine GDP by 2030
By 2030, the predicted growth of Machine-to-Machine GDP will fundamentally shift how users value device networks, as autonomous transactions between machines create new, measurable economic output. This expansion means that individual smart devices will not just consume data but will actively generate revenue through direct, uncatalyzed exchanges. For US users, this translates to higher ROI from automated value generation in connected infrastructure. Scaling a device-driven economy relies on this GDP growth to justify the cost of dense sensor deployment, as each machine becomes a micro-economic node that pays for its own operational existence.
- Machine-to-Machine GDP by 2030 will turn each connected device into a self-funding asset through autonomous payments.
- This growth directly offsets the hardware and energy costs of scaling device fleets for US economy-of-things deployments.
- Users will see real-time profit sharing from machines that negotiate and transact without human input by 2030.
- The predicted GDP rise by 2030 reduces reliance on external data-subscription models for device-driven infrastructure.
Evolution of Insurance Models for Connected Assets
Insurance for connected assets has shifted from static, annual policies to real-time, usage-based coverage models. These evolving models now use device telemetry to assess risk dynamically, allowing premiums to change based on actual behavior, environmental data, and asset condition. For connected devices in the Economy of Things, this means coverage triggers Topio automatically when an asset is in use and pauses when idle, eliminating blanket costs. The models also enable parametric payouts, where damage verification via sensor data instantly initiates claims without human intervention, directly aligning insurance cost with asset lifecycle and utilization.
- Premiums calculated per operating hour rather than fixed policy periods.
- Claims automatously triggered by sensor-verified threshold breaches.
- Coverage boundaries adjusted via geofencing and real-time asset location data.
- Risk pools formed from aggregated device telemetry to reward safe usage patterns.
Role of AI in Optimizing Device-Driven Economic Decisions
AI optimizes device-driven economic decisions by analyzing real-time data streams from interconnected machines to dynamically adjust pricing and resource allocation. It evaluates micro-transactions for smart appliances or industrial sensors, ensuring minimal latency and maximal value per exchange. The system must balance predictive maintenance schedules against spot-market energy pricing to avoid conflicting profit signals. This enables autonomous negotiation between devices for service priority, such as a factory robot bidding for electricity during peak hours. Predictive cost optimization across device fleets reduces waste while maintaining operational throughput, directly addressing scalability bottlenecks in decentralized economic networks.